File size: 124,089 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 |
{
"paper_id": "2021",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T02:10:00.170305Z"
},
"title": "Image Retrieval for Arguments Using Stance-Aware Query Expansion",
"authors": [
{
"first": "Johannes",
"middle": [],
"last": "Kiesel",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Bauhaus-Universit\u00e4t Weimar",
"location": {}
},
"email": "johannes.kiesel@uni-weimar.de"
},
{
"first": "Nico",
"middle": [],
"last": "Reichenbach",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Leipzig University",
"location": {}
},
"email": "nico.reichenbach@posteo.de"
},
{
"first": "Benno",
"middle": [],
"last": "Stein",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Universit\u00e4t Weimar",
"location": {}
},
"email": "benno.stein@uni-weimar.de"
},
{
"first": "Martin",
"middle": [],
"last": "Potthast",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Leipzig University",
"location": {}
},
"email": "martin.potthast@uni-leipzig.de"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far. This paper shows how to integrate images into argument mining research, specifically into argument retrieval. By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve \"argumentative images\" for the respective stance. Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance. An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.",
"pdf_parse": {
"paper_id": "2021",
"_pdf_hash": "",
"abstract": [
{
"text": "Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far. This paper shows how to integrate images into argument mining research, specifically into argument retrieval. By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve \"argumentative images\" for the respective stance. Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance. An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Images are a prominent form of non-verbal communication. Images convey messages in diverse ways including scribbles, concept drawings, (produced) photos, data visualizations, or internet memes. Moreover, images can serve as an inspiration or an overview of the topic at hand. They play a key role in public discourse (Woods and Hahner, 2019) and in expressing personal opinion (Heiskanen, 2017) , e.g., in the form of political memes. Spread \"virally,\" they can gain a large followership on social media and influence political decision-making (Watt, 2015) or serve as a form of evidence for troublesome events. For example, about 150,000 images are uploaded to Facebook every minute. 1 All of these uses often form an integral part of argumentative writing or speechmaking. Composing an argumentative text or speech hence does not only include the retrieval and arrangement of written arguments, but also that of relevant \"argumentative images\" to go along with them.",
"cite_spans": [
{
"start": 317,
"end": 341,
"text": "(Woods and Hahner, 2019)",
"ref_id": "BIBREF39"
},
{
"start": 377,
"end": 394,
"text": "(Heiskanen, 2017)",
"ref_id": "BIBREF20"
},
{
"start": 544,
"end": 556,
"text": "(Watt, 2015)",
"ref_id": "BIBREF37"
},
{
"start": 685,
"end": 686,
"text": "1",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "1 https://www.domo.com/learn/data-never-sleeps-8",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Figure 1: Mock-up of an image search engine for arguments, featuring a query box, checkboxes to filter, and results categorized as pro (left) and con (right) images.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "However, though the retrieval of written arguments has become a well-established line of research, the paper at hand is the first of its kind to define and tackle the task of retrieving \"argumentative images.\" Inspired by existing argument search interfaces, Figure 1 illustrates how an image search engine for arguments might look.",
"cite_spans": [],
"ref_spans": [
{
"start": 259,
"end": 267,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "By proposing stance-aware query expansion, this paper shows how to harness existing keywordbased image retrieval technology for the task of retrieving images for arguments. The key assumption of this expansion is that adding terms to the user's query that indicate a stance (i.e., pro or con the query's underlying issue), then images retrieved for the expanded queries support that stance. Figure 1 illustrates the desired effect: the shown images stem from Google's image search after having expanded the query nuclear energy with either the term good (images on the left-hand side) or the term anti (right-hand side).",
"cite_spans": [],
"ref_spans": [
{
"start": 391,
"end": 397,
"text": "Figure",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In what follows, Section 3 introduces the task of \"argumentative image\" retrieval, Section 4 outlines our approach to this task-stance-aware query expansion-including three methodological variants, and the Sections 5 and 6 detail important elements of our experimental setting as well as evaluation results for the three proposed methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Al-Khatib, 2019 defines an argumentation strategy as \"a set of principles that guides the selection and arrangement of arguments (plus contextual information) in an argumentative discourse.\" While research in computational argumentation has so far focused on verbal argumentation, real-world discourse often integrates images to great effect, ranging from memes emerging on the spur of of a moment to figures summarizing months of research. Discourse analyses thus often include images (e.g., Frohmann, 1992; Farkas and Bene, 2020) .",
"cite_spans": [
{
"start": 493,
"end": 508,
"text": "Frohmann, 1992;",
"ref_id": "BIBREF17"
},
{
"start": 509,
"end": 531,
"text": "Farkas and Bene, 2020)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Images for Arguments Images can provide contextual information and express, underline, or popularize an opinion (Dove, 2012) , thereby taking the form of subjective statements (Dunaway, 2018) . Some images express both a premise and a conclusion, making them full arguments (Roque, 2012; Grancea, 2017) . Other images may provide contextual information only and need to be combined with a conclusion to form an argument. In this regard, a recent SemEval task distinguished a total of 22 persuasion techniques in memes alone (Dimitrov et al., 2021) . Moreover, argument quality dimensions like acceptability, credibility, emotional appeal, and sufficiency (Wachsmuth et al., 2017a) all apply to arguments that include images as well. And as a kind of visual argumentation scheme (a \"stereotypical pattern of human reasoning\"; Walton et al., 2008) , some images are frequently adapted to different topics (Heiskanen, 2017) . Social groups even create their own symbolisms and use them to express opinions (e.g., fringe web communities; Zannettou et al., 2018) . The potentially high emotional impact of images to a vast audience (Adler-Nissen et al., 2020) can cause changes in the social discourse and eventually politics (Woods and Hahner, 2019). Examples include photos of a drowned refugee child (Barnard and Shoumali, 2015; D'Orazio, 2015) , police violence (Berger, 2010) , or the recent departure of Western military forces from Afghanistan.",
"cite_spans": [
{
"start": 112,
"end": 124,
"text": "(Dove, 2012)",
"ref_id": "BIBREF13"
},
{
"start": 176,
"end": 191,
"text": "(Dunaway, 2018)",
"ref_id": "BIBREF14"
},
{
"start": 274,
"end": 287,
"text": "(Roque, 2012;",
"ref_id": "BIBREF25"
},
{
"start": 288,
"end": 302,
"text": "Grancea, 2017)",
"ref_id": "BIBREF19"
},
{
"start": 524,
"end": 547,
"text": "(Dimitrov et al., 2021)",
"ref_id": null
},
{
"start": 655,
"end": 680,
"text": "(Wachsmuth et al., 2017a)",
"ref_id": "BIBREF31"
},
{
"start": 825,
"end": 845,
"text": "Walton et al., 2008)",
"ref_id": null
},
{
"start": 903,
"end": 920,
"text": "(Heiskanen, 2017)",
"ref_id": "BIBREF20"
},
{
"start": 1034,
"end": 1057,
"text": "Zannettou et al., 2018)",
"ref_id": "BIBREF42"
},
{
"start": 1127,
"end": 1154,
"text": "(Adler-Nissen et al., 2020)",
"ref_id": "BIBREF0"
},
{
"start": 1298,
"end": 1326,
"text": "(Barnard and Shoumali, 2015;",
"ref_id": "BIBREF5"
},
{
"start": 1327,
"end": 1342,
"text": "D'Orazio, 2015)",
"ref_id": "BIBREF12"
},
{
"start": 1361,
"end": 1375,
"text": "(Berger, 2010)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Image search Keyword-based image search analyzing the content of images or videos has been studied for decades (Aigrain et al., 1996) , pre-dated only by approaches relying on metadata and similarity measures (Chang and Fu, 1980) . In a recent survey, Latif et al. (2019) categorize image features into color, texture, shape, and spatial features. Current commercial search engines also index text found in images, surrounding text, alternative texts displayed when an image is unavailable, and their URLs (Wu, 2020; Google, 2021) . Also related to the retrieval of argumentative images is that of \"emotional images\", which relies on image features like color and composition (Wang and He, 2008; Solli and Lenz, 2011) . Argumentation goes hand in hand with emotions, so that emotional features may be promising for retrieving images for arguments in the future. For lack of labeled data (argumentativeness plus emotionality), we start by exploiting keyword-based web search to retrieve images for arguments from the web, as did the earliest image search approaches (e.g., Yanai, 2001 ).",
"cite_spans": [
{
"start": 111,
"end": 133,
"text": "(Aigrain et al., 1996)",
"ref_id": "BIBREF3"
},
{
"start": 209,
"end": 229,
"text": "(Chang and Fu, 1980)",
"ref_id": "BIBREF9"
},
{
"start": 252,
"end": 271,
"text": "Latif et al. (2019)",
"ref_id": "BIBREF22"
},
{
"start": 506,
"end": 516,
"text": "(Wu, 2020;",
"ref_id": "BIBREF40"
},
{
"start": 517,
"end": 530,
"text": "Google, 2021)",
"ref_id": null
},
{
"start": 676,
"end": 695,
"text": "(Wang and He, 2008;",
"ref_id": "BIBREF36"
},
{
"start": 696,
"end": 717,
"text": "Solli and Lenz, 2011)",
"ref_id": "BIBREF27"
},
{
"start": 1072,
"end": 1083,
"text": "Yanai, 2001",
"ref_id": "BIBREF41"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Argument search Based on argument mining from texts (cf. Peldszus and Stede, 2013) , argument search engines aim to support decisionmaking and persuasion. Conceptually, a query to an argument search engine may either name an issue without a stance (Stab et al., 2018 ) (e.g., nuclear energy), or represent a conclusion for which supporting and attacking premises are to be retrieved (e.g., nuclear energy mitigates climate change). The first collection of arguments from the web is the Internet Argument Corpus (Walker et al., 2012), containing 400,000 posts from an online debate portal. The first argument search engine, args.me, indexes a similar dataset of arguments (Wachsmuth et al., 2017b) . Not relying on retrieval in collections of arguments, Ar-gumenText (Stab et al., 2018) first searches for documents relevant to a user's query in generic web crawls, and then mines arguments on the fly within retrieved documents. Regarding the evaluation of argument search engines, judging the topic relevance of a retrieved text alone is insufficient, it must also be argumentative (Potthast et al., 2019; Bondarenko et al., 2021) . Research on argumentation has identified many further quality criteria for arguments (Wachsmuth et al., 2017a ), yet few have been investigated for argument retrieval, and hardly anything has been said on the argumentative quality of images.",
"cite_spans": [
{
"start": 57,
"end": 82,
"text": "Peldszus and Stede, 2013)",
"ref_id": "BIBREF23"
},
{
"start": 248,
"end": 266,
"text": "(Stab et al., 2018",
"ref_id": "BIBREF28"
},
{
"start": 671,
"end": 696,
"text": "(Wachsmuth et al., 2017b)",
"ref_id": "BIBREF32"
},
{
"start": 766,
"end": 785,
"text": "(Stab et al., 2018)",
"ref_id": "BIBREF28"
},
{
"start": 1083,
"end": 1106,
"text": "(Potthast et al., 2019;",
"ref_id": "BIBREF24"
},
{
"start": 1107,
"end": 1131,
"text": "Bondarenko et al., 2021)",
"ref_id": "BIBREF8"
},
{
"start": 1219,
"end": 1243,
"text": "(Wachsmuth et al., 2017a",
"ref_id": "BIBREF31"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Similar to textual argument retrieval, we define the task of retrieving images for arguments as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "Given a keyword query suggesting an issue or a claim for a topic, retrieve as two ranked lists those and only those images that can assist someone in (1) supporting and (2) attacking it.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "This definition presumes that a given user intends to search for images suitable to assist in persuading others, an intent that encompasses a diversity of real-life scenarios, such as sending a pertinent image to a friend, using it as a cover for a news article or blog post, or on a slide in a presentation. Furthermore, it encompasses deliberation scenarios, such as forming an own opinion, and creating a collage for a school project. We expect search engines for argumentative images to offer facets as shown in Figure 1 to meet such needs.",
"cite_spans": [],
"ref_spans": [
{
"start": 516,
"end": 524,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "To assess the relevance of an image to a given query, a three-fold judgment is required:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "Topic relevance The image content is related to the query topic. This criterion corresponds to the notion of relevance in keyword-based image retrieval (Shanbehzadeh et al., 2000) .",
"cite_spans": [
{
"start": 152,
"end": 179,
"text": "(Shanbehzadeh et al., 2000)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "Argumentativeness The image can be used to support a stance regarding the query topic. This criterion corresponds to the notion of a context-dependent claim in textual argument mining (Aharoni et al., 2014) .",
"cite_spans": [
{
"start": 184,
"end": 206,
"text": "(Aharoni et al., 2014)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "Stance relevance The image can be used to support the predicted stance within the query topic. This criterion corresponds to the categorization into pros and cons in standard argument search (Wachsmuth et al., 2017b ).",
"cite_spans": [
{
"start": 191,
"end": 215,
"text": "(Wachsmuth et al., 2017b",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "Since stance relevance entails argumentativeness, which in turn entails topic relevance, we refer to these three as \"levels\" of relevance. Though previous work focused on stance relevance only (e.g., Stab et al., 2018) , an analysis on all three levels provides more insight into the errors made and is especially warranted for \"argumentative images.\" Figure 2 illustrates the different levels for the example query nuclear energy, showing images that fail (a) topic-relevance, (b) argumentativeness, and (c) stance-relevance (provided the image is categorized as a con). Though also many textual arguments appeal to emotion rather than logic, images are especially suited for such an appeal. However, as the emotions invoked can depend on both the viewer and context, it can be surprisingly unclear whether an image is argumentative or to which stance it is relevant. Consider Figure 2d . The meme image mixes the question of nuclear energy with the emotions towards a certain political moment (US president Trump's 2020 State of the Union Address, where, at its conclusion, the Speaker of the House of Representatives Pelosi tore up its official copy as a symbolic comment on its contents, Stewart, 2020) . Depending on the own emotions towards the shown politicians and statement, one can read the image as pro nuclear energy (\"society\" not caring for facts) or against (\"society\" ripping up lies). In the case of such images with ambiguous stance, the definition of stance relevance above suggests listing the image both as a pro and a con. In the future, however, additional considerations of argumentative quality (especially in terms of clarity) might suggest to omit such images completely, or to show them separately.",
"cite_spans": [
{
"start": 200,
"end": 218,
"text": "Stab et al., 2018)",
"ref_id": "BIBREF28"
},
{
"start": 1192,
"end": 1206,
"text": "Stewart, 2020)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [
{
"start": 352,
"end": 360,
"text": "Figure 2",
"ref_id": "FIGREF0"
},
{
"start": 878,
"end": 887,
"text": "Figure 2d",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Image Retrieval for Arguments",
"sec_num": "3"
},
{
"text": "The basic hypothesis of our approach is that the task of image retrieval for arguments can be tackled effectively by the structure shown in Figure 3a , Figure 3 : (a) Generic structure of an image search engine for arguments using a keyword-based image search engine and (b) the more specific structure of stance-aware query expansion employed in this paper. In the generic structure, the user's query q is expanded to n queries q 1 , . . . , q n , a result list R i is retrieved for each q i , which are classified and re-ranked to create the lists of pro (R + ) and con images (R \u2212 ). Structure (b) corresponds to a particular case without classification and re-ranking, where different sets of expanded queries, q + 1 , . . . , q + n and q \u2212 1 , . . . , q \u2212 m , are used to independently create R + and R \u2212 . which issues for a user's query several expanded queries to a keyword-based image search engine and then fuses the result lists to a list of pro and con images. However, we assume that the semantic capabilities of modern keyword-based image search engines can be harnessed further to already provide a classification of the images, thus separating pro and con images as well as omitting neutral ones.",
"cite_spans": [],
"ref_spans": [
{
"start": 140,
"end": 149,
"text": "Figure 3a",
"ref_id": null
},
{
"start": 152,
"end": 160,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "In particular we propose a stance-aware query expansion as depicted in Figure 3b . It generates focused queries for both relevant stances (superscript + for pro and \u2212 for con), which are processed independently of each other, presuming that a sufficient diversity of expanded stance-aware queries recalls relevant images for each stance on the top ranks. In that case, the development of a post-retrieval image stance classifier can be omitted. The result lists of each stance are interlaced, i.e., composed by taking the first result of each list for a stance, then the second of each, and so on.",
"cite_spans": [],
"ref_spans": [
{
"start": 71,
"end": 80,
"text": "Figure 3b",
"ref_id": null
}
],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "We devise three methods: (1) appending always the same stance-indicating terms to the user's query, (2) appending sentiment-indicating terms that co-occur with the query's terms, and (3) appending topic-specific stance-indicating terms obtained from a text argument search engine.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "(1) Good-Anti Conceivably the single most basic method is to expand the user's query with one term per stance. After some manual experiments, we opted for good as a pro term and anti as a con term. Another option for the latter was bad, but for some topics this term is more associated with \"doing it poorly\" than with \"being against it.\"",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "(2) Positive-Negative This method exploits the fact that stance is often reflected through expressions of sentiment, for example, as used in counterargument retrieval (Wachsmuth et al., 2018) . For each stance, we generate up to five queries by appending the top positive (for pro) or negative terms (for con) of the 8000 entries in the MPQA subjectivity lexicon (Wilson et al., 2005) as ranked by their co-occurrence with the query according to the Leipzig Corpora Collection's English corpus (120 million sentences, Goldhahn et al., 2012) .",
"cite_spans": [
{
"start": 167,
"end": 191,
"text": "(Wachsmuth et al., 2018)",
"ref_id": "BIBREF33"
},
{
"start": 363,
"end": 384,
"text": "(Wilson et al., 2005)",
"ref_id": "BIBREF38"
},
{
"start": 518,
"end": 540,
"text": "Goldhahn et al., 2012)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "(3) Pros-Cons This method employs argument search engines to identify terms typical for certain topic-stance-combinations. E.g., in arguments retrieved for nuclear energy, \"CO2 neutrality\" occurs more often in pro arguments than in con ones, whereas \"radiation\" occurs more often in con arguments than in pro ones. Based on work in anomaly detection (Afgani et al., 2008) , this method calculates the specificity of a term t to a stance s, \u03b4(t, s), as their contribution to the Kullback-Leibler divergence of the term distributions between the two stances:",
"cite_spans": [
{
"start": 350,
"end": 371,
"text": "(Afgani et al., 2008)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "\u03b4(t, s) = P(T= t|S = s) \u2022 log P(T= t|S = s) P(T= t|S = s) ,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "where P (T = t|S = s) is the probability of observing t given the stance s. This probability is estimated by word frequencies 2 in all arguments that an argument search engine-args.me 3 in our case (Wachsmuth et al., 2017b) -retrieves for the query and s. For preprocessing, we lemmatize both arguments and the query. Furthermore, we filter out all arguments from the website debate.org, as we found that its debate structure encourages to reference opposing points in arguments to counter them, which diminishes the \u03b4 of the respective terms. The method generates up to five queries for each stance by appending the top terms as ranked by \u03b4.",
"cite_spans": [
{
"start": 198,
"end": 223,
"text": "(Wachsmuth et al., 2017b)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Stance-Aware Query Expansion",
"sec_num": "4"
},
{
"text": "In retrieval tasks, human relevance judgments of retrieval results for a fixed set of topics allows for evaluating the effectiveness of competing retrieval models. Known as the Cranfield paradigm or TREC-style evaluation (Voorhees, 2001) , it is also employed in textual argument retrieval within the Touch\u00e9 shared tasks (Bondarenko et al., 2020) . All our expansion methods retrieve results for the same set of queries, which are then pooled and manually judged with respect to their relevance to a given query's information need.",
"cite_spans": [
{
"start": 221,
"end": 237,
"text": "(Voorhees, 2001)",
"ref_id": "BIBREF30"
},
{
"start": 321,
"end": 346,
"text": "(Bondarenko et al., 2020)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Crowdsourcing Relevance Judgements",
"sec_num": "5"
},
{
"text": "To evaluate the methods presented in Section 4, we employ a sample of 20 controversial questions from the Touch\u00e9 2020 Task 1 test set (Bondarenko et al., 2020) , from which we derive one query each. 4 We calculate the number of relevant images in the methods' ten top-ranked images (precision@10) for both rankings of pro and con images (thus actually for 20 images), which is straightforward to interpret for multiple relevance levels. To ensure state-of-the-art keyword-based retrieval and a large image index, all methods retrieve images using the Google image search. Figure 4 shows the annotation interface for gathering relevance judgments. As described in Section 3, we distinguish relevance on three levels: topic, argumentativeness, and stance. The first three options, the image being able to support the pro, contra, or both stances, indicate the image stance(s) and that it is argumentative and on topic. The fourth option, the image not being able to support a stance, indicates that the image is on-topic 2 To avoid zero probabilities, we employ standard add-one smoothing (Jurafsky and Martin, 2009 ).",
"cite_spans": [
{
"start": 134,
"end": 159,
"text": "(Bondarenko et al., 2020)",
"ref_id": "BIBREF7"
},
{
"start": 199,
"end": 200,
"text": "4",
"ref_id": null
},
{
"start": 1087,
"end": 1113,
"text": "(Jurafsky and Martin, 2009",
"ref_id": "BIBREF21"
}
],
"ref_spans": [
{
"start": 572,
"end": 580,
"text": "Figure 4",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Crowdsourcing Relevance Judgements",
"sec_num": "5"
},
{
"text": "3 API: https://www.args.me/api-en.html 4 From the original 49 topics, 13 were omitted for which one or more methods found no expansion terms, sampling 20 at random from the remainder to meet our labeling budget. Questions, queries, retrieved images, and annotations are available at https://doi.org/10.5281/zenodo.5202934. only. The final option indicates that the image has no stance and is neither argumentative nor on-topic.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Crowdsourcing Relevance Judgements",
"sec_num": "5"
},
{
"text": "Using the annotation interface of Figure 4 , we collected 2988 annotations on 993 images 5 and 20 topics from 12 layperson annotators: three annotators per image and topic. The images' order was randomized for each annotator within a topic to avoid order biases. The annotator agreement is fair to moderate (Fleiss' \u03ba of 0.39; Fleiss, 1981; Figure 7 shows \u03ba per topic). We compute a single label for each image and topic pair as per majority vote (two out of three), treating an annotation both as a vote for both pro and con, and assigning the groundtruth label both if there are at least two votes for both pro and con. 6 To ensure consistency in the face of difficulties reported by the annotators, we reviewed the ground-truth and changed the label for 223 images. The vast majority of changes (85%) were due to vagueness in our instructions: Annotators labeled images that can introduce the topic or question but have no argumentative value otherwise (see Figure 5 for examples) often as being able to support both stances though they should have labeled them as non-argumentative (neither). Figure 6 : Precision@10 achieved by the three query expansion methods at three relevance levels, averaged across 20 topics and each topic's individual pro and con rankings. The dashed lines indicate the expected precision@10 of a random image stance classifier dividing the retrieved images into the pro and con rankings. The bars are overlaid, not stacked.",
"cite_spans": [
{
"start": 307,
"end": 326,
"text": "(Fleiss' \u03ba of 0.39;",
"ref_id": null
},
{
"start": 327,
"end": 340,
"text": "Fleiss, 1981;",
"ref_id": "BIBREF16"
},
{
"start": 341,
"end": 349,
"text": "Figure 7",
"ref_id": null
}
],
"ref_spans": [
{
"start": 34,
"end": 42,
"text": "Figure 4",
"ref_id": "FIGREF1"
},
{
"start": 961,
"end": 969,
"text": "Figure 5",
"ref_id": "FIGREF2"
},
{
"start": 1097,
"end": 1105,
"text": "Figure 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "Crowdsourcing Relevance Judgements",
"sec_num": "5"
},
{
"text": "This section reports on a comparative evaluation of the three query expansions methods by their retrieval effectiveness, followed by a topic-wise error analysis. Last, we also carried out an investigation into the counterintuitive case of ambiguous images that can support both stances. Figure 6 shows the precision@10 for the three query expansion methods introduced in Section 4 with respect to the three levels of relevance assessed. Nearly all retrieved images can be expected to be relevant to the topic (92% to 95%). This being testimony to the effectiveness of Google's image search, it also indicates that the query expansion methods do not impair the keyword-based image search.",
"cite_spans": [],
"ref_spans": [
{
"start": 287,
"end": 295,
"text": "Figure 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "6"
},
{
"text": "The basic good-anti heuristic performs best, achieving an argumentative precision@10 of 0.64 and outperforming the pros-cons method by 0.12. An inspection of the pros-cons method's expanded terms reveals that they lack the stance-specificity of the other methods' terms. For example, the expansions money and person (pros-cons) are less specific than the expansions cheap and harm (positivenegative), as only the latter terms intrinsically convey a preference or stance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Overall Retrieval Effectiveness",
"sec_num": "6.1"
},
{
"text": "All methods lose 16 to 18 percentage points when being judged for stance-relevance, showing that 25% (good-anti) to 33% (pros-cons) of the retrieved images that are argumentative do support the opposite stance as intended. While for two of the three methods their stance precision@10 is better than a random stance assignment (dashed lines in Figure 6 ), it is far from perfect. Since Google image search is not public, it is challenging to pinpoint the source of the stance errors. A possible explanation may be that expansion terms found on an image's web page do not refer to being pro or con a given topic, but are used in a different context or even to convey the opposite stance.",
"cite_spans": [],
"ref_spans": [
{
"start": 343,
"end": 351,
"text": "Figure 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "Overall Retrieval Effectiveness",
"sec_num": "6.1"
},
{
"text": "We analyzed the retrieval effectiveness on a pertopic basis to learn which are the most challenging ones for retrieving argumentative images. It turns out that some topics are less well-suited to a keyword-based image retrieval (or to our query expansion methods). Moreover, some arguments are not suited to being illustrated or are for other reasons not found through image search. Figure 7 shows the retrieval effectiveness as precision@10 and the image stance distribution per topic.",
"cite_spans": [],
"ref_spans": [
{
"start": 383,
"end": 391,
"text": "Figure 7",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Topic-wise Error Analysis",
"sec_num": "6.2"
},
{
"text": "As the average precision@10 scores show, topic precision is high overall, except for the query standardized tests education (for issue: \"Do standardized tests improve education?\"). On closer inspection, we found that many off-topic images were either on education or standardized tests, possibly hinting at a lack of images that combine both.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Topic-wise Error Analysis",
"sec_num": "6.2"
},
{
"text": "Average argumentativeness precision@10, however, varies a great deal between 0.18 and 0.87. As an inspection of topics with low precision reveals, many of the retrieved images are wellsuited to introducing the topic, but not as argument support. For example, most images for body cameras police (\"Should body cameras be mandatory for police?\") show a police officer wearing a body camera. Similarly, many of the images for performance-enhancing drugs in sports (\"Should performance-enhancing drugs be accepted in sports?\") show sports equipment and syringes. For queries related to commercial products, like e-cigarettes (\"Is vaping with ecigarettes safe?\"), bottled water (\"Should bottled water be banned?\"), or school uniforms (\"Should students have to wear school uniforms?\"), many of the images are product photos from shopping or review pages and merely display the prod- uct. We assume that in these cases the expansion methods cannot sufficiently counter the search engine optimization (SEO) done by companies working in that domain, suggesting a pre-filtering by web document and/or image genre. Similarly, average stance precision@10 varies a lot between topics, ranging from 0.17 to 0.72. This variation is partly due to the large differences in the stance of the retrieved images, as illustrated in the right plot of Figure 7 . For some topics, argumentative precision and stance precision differ by as much as 0.30 (gun control and marijuana recreational use). Upon inspection, we noticed that the errors in the stance assignment dominantly occur in a single direction per topic. Though our setup intends to retrieve an equal amount of pros and cons per topic, the right plot of Figure 7 shows that the result set is often skewed in one direction. In the extreme, 95% of the retrieved argumentative images for animal testing are con only-which is plausible, as illustrating the benefits of animal testing is much more complex than showing animals being treated poorly. On the other hand, an unexpectedly high fraction of images are ambiguous and can be used to support both stances. For example, this is the case for nearly half of the retrieved argumentative images for euthanasia. Figure 8c shows one example for the topic.",
"cite_spans": [],
"ref_spans": [
{
"start": 1328,
"end": 1336,
"text": "Figure 7",
"ref_id": "FIGREF3"
},
{
"start": 1691,
"end": 1699,
"text": "Figure 7",
"ref_id": "FIGREF3"
},
{
"start": 2195,
"end": 2204,
"text": "Figure 8c",
"ref_id": "FIGREF5"
}
],
"eq_spans": [],
"section": "Topic-wise Error Analysis",
"sec_num": "6.2"
},
{
"text": "When 46% of the retrieved images can provide support for both stances, as for euthanasia, should an image search engine for arguments separate pro and con images in its search results as in Figure 1 ? To investigate this question, we inspected all 111 images that were judged ambiguous. Figure 8 exemplifies the image categories we identified. We found that most of the 111 images provide contextual information (78 images, 70%) in the form of geographic comparisons as in Figure 8a , statistics (especially polls), or forecasts. Similar categories include sources on the topic (10 images, 9%) and definitions (11 images, 10%). Images from these three categories (89% total) can strongly support an argumentation, even though different pieces of information they entail might lend support to different stances. Due to the benefits of such images, it thus makes sense to display them separately in a search interface, either on-demand or up front. As outliers, two other images contrast key points for pro and con, supporting mostly deliberation tasks. The remaining images were ambiguous in the way hypothesized before in that their stance changes depending on how one interprets them. Images of this kind will be a challenge to image retrieval for arguments, since their interpretation requires common sense and/or domain knowledge. ",
"cite_spans": [],
"ref_spans": [
{
"start": 190,
"end": 198,
"text": "Figure 1",
"ref_id": null
},
{
"start": 287,
"end": 295,
"text": "Figure 8",
"ref_id": "FIGREF5"
},
{
"start": 473,
"end": 482,
"text": "Figure 8a",
"ref_id": "FIGREF5"
}
],
"eq_spans": [],
"section": "On Images with Ambiguous Stance",
"sec_num": "6.3"
},
{
"text": "This paper introduces the task of image retrieval for arguments, proposes with stance-aware query expansion a family of methods to tackle the task that exploits existing keyword-based image search technology, and carries out a first empirical analysis for the task using human annotations on 993 images and 20 topics. In the experiments, the basic goodanti heuristic, which expands a user query with the same two terms (good for pro images, anti for con ones), outperforms more sophisticated query expansion approaches that employ terms specific to the query's topic. An error analysis unveils a high topic-dependence of the retrieval effectiveness. Finally, the paper investigates ambiguous images labeled as supporting both the pro and con stances.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "In cases where many neutral images are found, we suggest displaying them separately.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "Relying on Google's image search engine ensures a state-of-the-art retrieval model at the price of exact replicability. Since commercial image search engines are subject to frequent changes it must be investigated how reproducible our results are. Developing an open image retrieval system for arguments would enable laboratory evaluation. It would also allow for creating image representations tailored to argumentation, but requires creating and labeling an extensive image collection that covers a manifold of controversial topics.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "There is a lot of room to improve precision. Filtering images by analyzing their web pages may avoid ones that merely show a product or otherwise fail to make a point. Further analyses of the web pages with argument mining technologies could provide for a better stance classification of the images. Also images search engine feature to retrieve similar images may be helpful in this regard. Moreover, classifiers to identify persuasion techniques or emotions in images may prove beneficial to identify images for arguments. Assessing the quality of argumentative images could improve the imagined search engine's evaluation and utility to users. To foster research, we run a corresponding shared task to be held as part of the Touch\u00e9 2022 lab. 7 This paper seeks to extend argument mining in general and argument retrieval in particular to considering images. Other argument mining tasks (e.g., relation extraction in articles) could similarly be extended to account for the widespread use of images in argumentative texts or speechmaking.",
"cite_spans": [
{
"start": 745,
"end": 746,
"text": "7",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "Three images were retrieved for two topics. 6 E.g., if votes are off-topic, neither, pro, the result would be neither as two voted for on-topic but only one for a stance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://webis.de/events?q=touche#touche-2022",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Images, emotions, and international politics: the death of Alan Kurdi",
"authors": [
{
"first": "Rebecca",
"middle": [],
"last": "Adler-Nissen",
"suffix": ""
},
{
"first": "Katrine",
"middle": [
"Emilie"
],
"last": "Andersen",
"suffix": ""
},
{
"first": "Lene",
"middle": [],
"last": "Hansen",
"suffix": ""
}
],
"year": 2020,
"venue": "Review of International Studies",
"volume": "46",
"issue": "1",
"pages": "75--95",
"other_ids": {
"DOI": [
"10.1017/S0260210519000317"
]
},
"num": null,
"urls": [],
"raw_text": "Rebecca Adler-Nissen, Katrine Emilie Andersen, and Lene Hansen. 2020. Images, emotions, and interna- tional politics: the death of Alan Kurdi. Review of International Studies, 46(1):75-95.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Anomaly detection using the kullback-leibler divergence metric",
"authors": [
{
"first": "M",
"middle": [],
"last": "Afgani",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Sinanovic",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Haas",
"suffix": ""
}
],
"year": 2008,
"venue": "1st International Symposium on Applied Sciences on Biomedical and Communication Technologies (ISABEL 2008)",
"volume": "",
"issue": "",
"pages": "2325--5331",
"other_ids": {
"DOI": [
"10.1109/ISABEL.2008.4712573"
]
},
"num": null,
"urls": [],
"raw_text": "M. Afgani, S. Sinanovic, and H. Haas. 2008. Anomaly detection using the kullback-leibler divergence met- ric. In 1st International Symposium on Applied Sci- ences on Biomedical and Communication Technolo- gies (ISABEL 2008), pages 1-5. ISSN: 2325-5331.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics",
"authors": [
{
"first": "Ehud",
"middle": [],
"last": "Aharoni",
"suffix": ""
},
{
"first": "Anatoly",
"middle": [],
"last": "Polnarov",
"suffix": ""
},
{
"first": "Tamar",
"middle": [],
"last": "Lavee",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Hershcovich",
"suffix": ""
},
{
"first": "Ran",
"middle": [],
"last": "Levy",
"suffix": ""
},
{
"first": "Ruty",
"middle": [],
"last": "Rinott",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Gutfreund",
"suffix": ""
},
{
"first": "Noam",
"middle": [],
"last": "Slonim",
"suffix": ""
}
],
"year": 2014,
"venue": "1st Workshop on Argument Mining",
"volume": "",
"issue": "",
"pages": "64--68",
"other_ids": {
"DOI": [
"10.3115/v1/w14-2109"
]
},
"num": null,
"urls": [],
"raw_text": "Ehud Aharoni, Anatoly Polnarov, Tamar Lavee, Daniel Hershcovich, Ran Levy, Ruty Rinott, Dan Gutfre- und, and Noam Slonim. 2014. A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. In 1st Workshop on Argument Mining (ArgMining 2014), pages 64-68. ACL.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Content-based representation and retrieval of visual media: A state-of-the-art review",
"authors": [
{
"first": "Philippe",
"middle": [],
"last": "Aigrain",
"suffix": ""
},
{
"first": "Hongjiang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Dragutin",
"middle": [],
"last": "Petkovic",
"suffix": ""
}
],
"year": 1996,
"venue": "Multimedia Tools and Applications",
"volume": "3",
"issue": "3",
"pages": "179--202",
"other_ids": {
"DOI": [
"10.1007/BF00393937"
]
},
"num": null,
"urls": [],
"raw_text": "Philippe Aigrain, Hongjiang Zhang, and Dragutin Petkovic. 1996. Content-based representation and retrieval of visual media: A state-of-the-art review. Multimedia Tools and Applications, 3(3):179-202.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Computational Analysis of Argumentation Strategies. Dissertation",
"authors": [
{
"first": "Khalid",
"middle": [],
"last": "Al-Khatib",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Khalid Al-Khatib. 2019. Computational Analysis of Argumentation Strategies. Dissertation, Bauhaus- Universit\u00e4t Weimar.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Image of drowned syrian, aylan kurdi, 3, brings migrant crisis into focus. The New York Times",
"authors": [
{
"first": "Anne",
"middle": [],
"last": "Barnard",
"suffix": ""
},
{
"first": "Karam",
"middle": [],
"last": "Shoumali",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anne Barnard and Karam Shoumali. 2015. Image of drowned syrian, aylan kurdi, 3, brings migrant crisis into focus. The New York Times.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Fixing images: Civil rights photography and the struggle over representation",
"authors": [
{
"first": "A",
"middle": [],
"last": "Martin",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Berger",
"suffix": ""
}
],
"year": 2010,
"venue": "RIHA Journal",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Martin A. Berger. 2010. Fixing images: Civil rights photography and the struggle over representation. RIHA Journal, 10.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Overview of touch\u00e9 2020: Argument retrieval",
"authors": [
{
"first": "Alexander",
"middle": [],
"last": "Bondarenko",
"suffix": ""
},
{
"first": "Maik",
"middle": [],
"last": "Fr\u00f6be",
"suffix": ""
},
{
"first": "Meriem",
"middle": [],
"last": "Beloucif",
"suffix": ""
},
{
"first": "Lukas",
"middle": [],
"last": "Gienapp",
"suffix": ""
},
{
"first": "Yamen",
"middle": [],
"last": "Ajjour",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Panchenko",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Biemann",
"suffix": ""
},
{
"first": "Benno",
"middle": [],
"last": "Stein",
"suffix": ""
},
{
"first": "Henning",
"middle": [],
"last": "Wachsmuth",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "Potthast",
"suffix": ""
},
{
"first": "Matthias",
"middle": [],
"last": "Hagen",
"suffix": ""
}
],
"year": 2020,
"venue": "Working Notes Papers of the CLEF 2020 Evaluation Labs",
"volume": "2696",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexander Bondarenko, Maik Fr\u00f6be, Meriem Be- loucif, Lukas Gienapp, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, and Matthias Hagen. 2020. Overview of touch\u00e9 2020: Argument retrieval. In Working Notes Papers of the CLEF 2020 Evalu- ation Labs, volume 2696 of CEUR Workshop Pro- ceedings.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Overview of touch\u00e9 2021: Argument retrieval",
"authors": [
{
"first": "Alexander",
"middle": [],
"last": "Bondarenko",
"suffix": ""
},
{
"first": "Lukas",
"middle": [],
"last": "Gienapp",
"suffix": ""
},
{
"first": "Maik",
"middle": [],
"last": "Fr\u00f6be",
"suffix": ""
},
{
"first": "Meriem",
"middle": [],
"last": "Beloucif",
"suffix": ""
},
{
"first": "Yamen",
"middle": [],
"last": "Ajjour",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Panchenko",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Biemann",
"suffix": ""
},
{
"first": "Benno",
"middle": [],
"last": "Stein",
"suffix": ""
},
{
"first": "Henning",
"middle": [],
"last": "Wachsmuth",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "Potthast",
"suffix": ""
},
{
"first": "Matthias",
"middle": [],
"last": "Hagen",
"suffix": ""
}
],
"year": 2021,
"venue": "Advances in Information Retrieval. 43rd European Conference on IR Research (ECIR 2021)",
"volume": "12036",
"issue": "",
"pages": "574--582",
"other_ids": {
"DOI": [
"10.1007/978-3-030-72240-1_67"
]
},
"num": null,
"urls": [],
"raw_text": "Alexander Bondarenko, Lukas Gienapp, Maik Fr\u00f6be, Meriem Beloucif, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, and Matthias Ha- gen. 2021. Overview of touch\u00e9 2021: Argument retrieval. In Advances in Information Retrieval. 43rd European Conference on IR Research (ECIR 2021), volume 12036 of Lecture Notes in Computer Science, pages 574-582, Berlin Heidelberg New York. Springer.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Query-bypictorial-example",
"authors": [
{
"first": "",
"middle": [],
"last": "Ning-San",
"suffix": ""
},
{
"first": "King-Sun",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Fu",
"suffix": ""
}
],
"year": 1980,
"venue": "IEEE Transactions on Software Engineering",
"volume": "6",
"issue": "6",
"pages": "519--524",
"other_ids": {
"DOI": [
"10.1109/TSE.1980.230801"
]
},
"num": null,
"urls": [],
"raw_text": "Ning-San Chang and King-sun Fu. 1980. Query-by- pictorial-example. IEEE Transactions on Software Engineering, 6(6):519-524.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "SemEval-2021 task 6: Detection of persuasion techniques in texts and images",
"authors": [],
"year": null,
"venue": "15th International Workshop on Semantic Evaluation (SemEval'2021)",
"volume": "",
"issue": "",
"pages": "70--98",
"other_ids": {
"DOI": [
"10.18653/v1/2021.semeval-1.7"
]
},
"num": null,
"urls": [],
"raw_text": "SemEval-2021 task 6: Detection of persuasion tech- niques in texts and images. In 15th International Workshop on Semantic Evaluation (SemEval'2021), pages 70-98, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Journey of an image: from a beach in Bodrum to twenty million screens across the world",
"authors": [
{
"first": "D'",
"middle": [],
"last": "Francesco",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Orazio",
"suffix": ""
}
],
"year": 2015,
"venue": "The Iconic Image on Social Media: A Rapid Research Response to the Death of Aylan Kurdi. Visual Social Media Lab",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Francesco D'Orazio. 2015. Journey of an image: from a beach in Bodrum to twenty million screens across the world. In The Iconic Image on Social Media: A Rapid Research Response to the Death of Aylan Kurdi. Visual Social Media Lab.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "On images as evidence and arguments",
"authors": [
{
"first": "Ian",
"middle": [
"J"
],
"last": "Dove",
"suffix": ""
}
],
"year": 2012,
"venue": "Topical Themes in Argumentation Theory: Twenty Exploratory Studies, Argumentation Library",
"volume": "",
"issue": "",
"pages": "223--238",
"other_ids": {
"DOI": [
"10.1007/978-94-007-4041-9_15"
]
},
"num": null,
"urls": [],
"raw_text": "Ian J. Dove. 2012. On images as evidence and argu- ments. In Frans H. van Eemeren and Bart Garssen, editors, Topical Themes in Argumentation Theory: Twenty Exploratory Studies, Argumentation Library, pages 223-238. Springer Netherlands, Dordrecht.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Images, emotions, politics",
"authors": [
{
"first": "Finis",
"middle": [],
"last": "Dunaway",
"suffix": ""
}
],
"year": 2018,
"venue": "Modern American History",
"volume": "1",
"issue": "3",
"pages": "369--376",
"other_ids": {
"DOI": [
"10.1017/mah.2018.17"
]
},
"num": null,
"urls": [],
"raw_text": "Finis Dunaway. 2018. Images, emotions, politics. Modern American History, 1(3):369-376.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Images, politicians, and social media: Patterns and effects of politicians' image-based political communication strategies on social media",
"authors": [
{
"first": "X\u00e9nia",
"middle": [],
"last": "Farkas",
"suffix": ""
},
{
"first": "M\u00e1rton",
"middle": [],
"last": "Bene",
"suffix": ""
}
],
"year": 2020,
"venue": "The International Journal of Press/Politics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.1177/1940161220959553"
]
},
"num": null,
"urls": [],
"raw_text": "X\u00e9nia Farkas and M\u00e1rton Bene. 2020. Images, politi- cians, and social media: Patterns and effects of politicians' image-based political communication strategies on social media. The International Jour- nal of Press/Politics.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "The measurement of interrater agreement",
"authors": [
{
"first": "J",
"middle": [
"L"
],
"last": "Fleiss",
"suffix": ""
}
],
"year": 1981,
"venue": "Statistical methods for rates and proportions",
"volume": "",
"issue": "",
"pages": "212--236",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. L. Fleiss. 1981. The measurement of interrater agree- ment. In Statistical methods for rates and propor- tions, 2 edition, pages 212-236. John Wiley & Sons, New York.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "The power of images: A discourse analysis of the cognitive viewpoint",
"authors": [
{
"first": "Bernd",
"middle": [],
"last": "Frohmann",
"suffix": ""
}
],
"year": 1992,
"venue": "Journal of Documentation",
"volume": "48",
"issue": "4",
"pages": "365--386",
"other_ids": {
"DOI": [
"10.1108/eb026904"
]
},
"num": null,
"urls": [],
"raw_text": "Bernd Frohmann. 1992. The power of images: A dis- course analysis of the cognitive viewpoint. Journal of Documentation, 48(4):365-386.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Building large monolingual dictionaries at the leipzig corpora collection: From 100 to 200 languages",
"authors": [
{
"first": "Dirk",
"middle": [],
"last": "Goldhahn",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "Eckart",
"suffix": ""
},
{
"first": "Uwe",
"middle": [],
"last": "Quasthoff",
"suffix": ""
}
],
"year": 2012,
"venue": "Eighth International Conference on Language Resources and Evaluation (LREC 2012)",
"volume": "",
"issue": "",
"pages": "759--765",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dirk Goldhahn, Thomas Eckart, and Uwe Quasthoff. 2012. Building large monolingual dictionaries at the leipzig corpora collection: From 100 to 200 languages. In Eighth International Conference on Language Resources and Evaluation (LREC 2012), pages 759-765. European Language Resources As- sociation (ELRA).",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Types of visual arguments. Argumentum",
"authors": [
{
"first": "Ioana",
"middle": [],
"last": "Grancea",
"suffix": ""
}
],
"year": 2017,
"venue": "Journal of the Seminar of Discursive Logic, Argumentation Theory and Rhetoric",
"volume": "15",
"issue": "2",
"pages": "16--34",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ioana Grancea. 2017. Types of visual arguments. Argu- mentum. Journal of the Seminar of Discursive Logic, Argumentation Theory and Rhetoric, 15(2):16-34.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Meme-ing electoral participation",
"authors": [
{
"first": "Benita",
"middle": [],
"last": "Heiskanen",
"suffix": ""
}
],
"year": 2017,
"venue": "European journal of American studies",
"volume": "12",
"issue": "2",
"pages": "",
"other_ids": {
"DOI": [
"10.4000/ejas.12158"
]
},
"num": null,
"urls": [],
"raw_text": "Benita Heiskanen. 2017. Meme-ing electoral participa- tion. European journal of American studies, 12(2).",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2 edition. Prentice Hall series in artificial intelligence",
"authors": [
{
"first": "Dan",
"middle": [],
"last": "Jurafsky",
"suffix": ""
},
{
"first": "James",
"middle": [
"H"
],
"last": "Martin",
"suffix": ""
}
],
"year": 2009,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dan Jurafsky and James H. Martin. 2009. Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2 edition. Prentice Hall series in artificial intelligence. Prentice Hall.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Naeem Iqbal Ratyal, Bushra Zafar, Saadat Hanif Dar, Muhammad Sajid, and Tehmina Khalil. 2019. Content-based image retrieval and feature extraction: A comprehensive review",
"authors": [
{
"first": "Afshan",
"middle": [],
"last": "Latif",
"suffix": ""
},
{
"first": "Aqsa",
"middle": [],
"last": "Rasheed",
"suffix": ""
},
{
"first": "Umer",
"middle": [],
"last": "Sajid",
"suffix": ""
},
{
"first": "Jameel",
"middle": [],
"last": "Ahmed",
"suffix": ""
},
{
"first": "Nouman",
"middle": [],
"last": "Ali",
"suffix": ""
}
],
"year": 2019,
"venue": "Mathematical Problems in Engineering",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.1155/2019/9658350"
]
},
"num": null,
"urls": [],
"raw_text": "Afshan Latif, Aqsa Rasheed, Umer Sajid, Jameel Ahmed, Nouman Ali, Naeem Iqbal Ratyal, Bushra Zafar, Saadat Hanif Dar, Muhammad Sajid, and Tehmina Khalil. 2019. Content-based image re- trieval and feature extraction: A comprehensive review. Mathematical Problems in Engineering, 2019:21.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "From argument diagrams to argumentation mining in texts: A survey",
"authors": [
{
"first": "Andreas",
"middle": [],
"last": "Peldszus",
"suffix": ""
},
{
"first": "Manfred",
"middle": [],
"last": "Stede",
"suffix": ""
}
],
"year": 2013,
"venue": "International Journal of Cognitive Informatics and Natural Intelligence",
"volume": "7",
"issue": "1",
"pages": "1--31",
"other_ids": {
"DOI": [
"10.4018/jcini.2013010101"
]
},
"num": null,
"urls": [],
"raw_text": "Andreas Peldszus and Manfred Stede. 2013. From ar- gument diagrams to argumentation mining in texts: A survey. International Journal of Cognitive Infor- matics and Natural Intelligence, 7(1):1-31.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Argument search: Assessing argument relevance",
"authors": [
{
"first": "Martin",
"middle": [],
"last": "Potthast",
"suffix": ""
},
{
"first": "Lukas",
"middle": [],
"last": "Gienapp",
"suffix": ""
},
{
"first": "Florian",
"middle": [],
"last": "Euchner",
"suffix": ""
},
{
"first": "Nick",
"middle": [],
"last": "Heilenk\u00f6tter",
"suffix": ""
},
{
"first": "Nico",
"middle": [],
"last": "Weidmann",
"suffix": ""
},
{
"first": "Henning",
"middle": [],
"last": "Wachsmuth",
"suffix": ""
},
{
"first": "Benno",
"middle": [],
"last": "Stein",
"suffix": ""
},
{
"first": "Matthias",
"middle": [],
"last": "Hagen",
"suffix": ""
}
],
"year": 2019,
"venue": "42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.1145/3331184.3331327"
]
},
"num": null,
"urls": [],
"raw_text": "Martin Potthast, Lukas Gienapp, Florian Euchner, Nick Heilenk\u00f6tter, Nico Weidmann, Henning Wachsmuth, Benno Stein, and Matthias Hagen. 2019. Argument search: Assessing argument relevance. In 42nd In- ternational ACM Conference on Research and De- velopment in Information Retrieval (SIGIR 2019). ACM.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Visual argumentation: A further reappraisal",
"authors": [
{
"first": "Georges",
"middle": [],
"last": "Roque",
"suffix": ""
}
],
"year": 2012,
"venue": "Springer Netherlands, Dordrecht. Series Title: Argumentation Library",
"volume": "22",
"issue": "",
"pages": "273--288",
"other_ids": {
"DOI": [
"10.1007/978-94-007-4041-9_18"
]
},
"num": null,
"urls": [],
"raw_text": "Georges Roque. 2012. Visual argumentation: A fur- ther reappraisal. In Frans H. van Eemeren and Bart Garssen, editors, Topical Themes in Argumentation Theory, volume 22, pages 273-288. Springer Nether- lands, Dordrecht. Series Title: Argumentation Li- brary.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Image indexing and retrieval techniques: past, present, and next",
"authors": [
{
"first": "Jamshid",
"middle": [],
"last": "Shanbehzadeh",
"suffix": ""
},
{
"first": "Fariborz",
"middle": [],
"last": "Amir-Masoud Eftekhari-Moghadam",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mahmoudi",
"suffix": ""
}
],
"year": 2000,
"venue": "Storage and Retrieval for Media Databases",
"volume": "3972",
"issue": "",
"pages": "461--470",
"other_ids": {
"DOI": [
"10.1117/12.373578"
]
},
"num": null,
"urls": [],
"raw_text": "Jamshid Shanbehzadeh, Amir-Masoud Eftekhari- Moghadam, and Fariborz Mahmoudi. 2000. Image indexing and retrieval techniques: past, present, and next. In Storage and Retrieval for Media Databases 2000, volume 3972 of SPIE Proceedings, pages 461-470. SPIE.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Color emotions for multi-colored images",
"authors": [
{
"first": "Martin",
"middle": [],
"last": "Solli",
"suffix": ""
},
{
"first": "Reiner",
"middle": [],
"last": "Lenz",
"suffix": ""
}
],
"year": 2011,
"venue": "Color Research & Application",
"volume": "36",
"issue": "3",
"pages": "210--221",
"other_ids": {
"DOI": [
"10.1002/col.20604"
]
},
"num": null,
"urls": [],
"raw_text": "Martin Solli and Reiner Lenz. 2011. Color emotions for multi-colored images. Color Research & Appli- cation, 36(3):210-221.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "ArgumenText: Searching for arguments in heterogeneous sources",
"authors": [
{
"first": "Christian",
"middle": [],
"last": "Stab",
"suffix": ""
},
{
"first": "Johannes",
"middle": [],
"last": "Daxenberger",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Stahlhut",
"suffix": ""
},
{
"first": "Tristan",
"middle": [],
"last": "Miller",
"suffix": ""
},
{
"first": "Benjamin",
"middle": [],
"last": "Schiller",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Tauchmann",
"suffix": ""
},
{
"first": "Steffen",
"middle": [],
"last": "Eger",
"suffix": ""
},
{
"first": "Iryna",
"middle": [],
"last": "Gurevych",
"suffix": ""
}
],
"year": 2018,
"venue": "Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2018)",
"volume": "",
"issue": "",
"pages": "21--25",
"other_ids": {
"DOI": [
"10.18653/v1/n18-5005"
]
},
"num": null,
"urls": [],
"raw_text": "Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, and Iryna Gurevych. 2018. ArgumenText: Searching for arguments in heteroge- neous sources. In Conference of the North American Chapter of the Association for Computational Lin- guistics (NAACL-HLT 2018), pages 21-25. ACL.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Why nancy pelosi ripping up some papers has set the internet on fire",
"authors": [
{
"first": "Emily",
"middle": [],
"last": "Stewart",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Emily Stewart. 2020. Why nancy pelosi ripping up some papers has set the internet on fire. Vox.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "The philosophy of information retrieval evaluation",
"authors": [
{
"first": "Ellen",
"middle": [
"M"
],
"last": "Voorhees",
"suffix": ""
}
],
"year": 2001,
"venue": "Second Workshop of the Cross-Language Evaluation Forum",
"volume": "2406",
"issue": "",
"pages": "355--370",
"other_ids": {
"DOI": [
"10.1007/3-540-45691-0_34"
]
},
"num": null,
"urls": [],
"raw_text": "Ellen M. Voorhees. 2001. The philosophy of infor- mation retrieval evaluation. In Second Workshop of the Cross-Language Evaluation Forum, CLEF 2001, volume 2406 of Lecture Notes in Computer Science, pages 355-370. Springer.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Computational argumentation quality assessment in natural language",
"authors": [
{
"first": "Henning",
"middle": [],
"last": "Wachsmuth",
"suffix": ""
},
{
"first": "Nona",
"middle": [],
"last": "Naderi",
"suffix": ""
},
{
"first": "Yufang",
"middle": [],
"last": "Hou",
"suffix": ""
},
{
"first": "Yonatan",
"middle": [],
"last": "Bilu",
"suffix": ""
},
{
"first": "Vinodkumar",
"middle": [],
"last": "Prabhakaran",
"suffix": ""
},
{
"first": "Tim",
"middle": [
"Alberdingk"
],
"last": "Thijm",
"suffix": ""
},
{
"first": "Graeme",
"middle": [],
"last": "Hirst",
"suffix": ""
},
{
"first": "Benno",
"middle": [],
"last": "Stein",
"suffix": ""
}
],
"year": 2017,
"venue": "15th Conference of the European Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "176--187",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Henning Wachsmuth, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Al- berdingk Thijm, Graeme Hirst, and Benno Stein. 2017a. Computational argumentation quality assess- ment in natural language. In 15th Conference of the European Chapter of the Association for Computa- tional Linguistics (EACL 2017), pages 176-187.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Building an argument search engine for the web",
"authors": [
{
"first": "Henning",
"middle": [],
"last": "Wachsmuth",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "Potthast",
"suffix": ""
},
{
"first": "Khalid",
"middle": [
"Al"
],
"last": "Khatib",
"suffix": ""
},
{
"first": "Yamen",
"middle": [],
"last": "Ajjour",
"suffix": ""
},
{
"first": "Jana",
"middle": [],
"last": "Puschmann",
"suffix": ""
},
{
"first": "Jiani",
"middle": [],
"last": "Qu",
"suffix": ""
},
{
"first": "Jonas",
"middle": [],
"last": "Dorsch",
"suffix": ""
},
{
"first": "Viorel",
"middle": [],
"last": "Morari",
"suffix": ""
},
{
"first": "Janek",
"middle": [],
"last": "Bevendorff",
"suffix": ""
},
{
"first": "Benno",
"middle": [],
"last": "Stein",
"suffix": ""
}
],
"year": 2017,
"venue": "4th Workshop on Argument Mining",
"volume": "",
"issue": "",
"pages": "49--59",
"other_ids": {
"DOI": [
"10.18653/v1/W17-5106"
]
},
"num": null,
"urls": [],
"raw_text": "Henning Wachsmuth, Martin Potthast, Khalid Al Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. 2017b. Building an argument search engine for the web. In 4th Workshop on Argument Mining (ArgMining 2017), pages 49-59, Copenhagen, Denmark. Association for Computational Linguistics.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Retrieval of the best counterargument without prior topic knowledge",
"authors": [
{
"first": "Henning",
"middle": [],
"last": "Wachsmuth",
"suffix": ""
},
{
"first": "Shahbaz",
"middle": [],
"last": "Syed",
"suffix": ""
},
{
"first": "Benno",
"middle": [],
"last": "Stein",
"suffix": ""
}
],
"year": 2018,
"venue": "56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)",
"volume": "",
"issue": "",
"pages": "241--251",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Henning Wachsmuth, Shahbaz Syed, and Benno Stein. 2018. Retrieval of the best counterargument with- out prior topic knowledge. In 56th Annual Meet- ing of the Association for Computational Linguistics (ACL 2018), pages 241-251. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "A corpus for research on deliberation and debate",
"authors": [
{
"first": "A",
"middle": [],
"last": "Marilyn",
"suffix": ""
},
{
"first": "Pranav",
"middle": [],
"last": "Walker",
"suffix": ""
},
{
"first": "Jean E Fox",
"middle": [],
"last": "Anand",
"suffix": ""
},
{
"first": "Rob",
"middle": [],
"last": "Tree",
"suffix": ""
},
{
"first": "Joseph",
"middle": [],
"last": "Abbott",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "King",
"suffix": ""
}
],
"year": 2012,
"venue": "8th International Conference on Language Resources and Evaluation (LREC 2012)",
"volume": "",
"issue": "",
"pages": "812--817",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Marilyn A Walker, Pranav Anand, Jean E Fox Tree, Rob Abbott, and Joseph King. 2012. A corpus for research on deliberation and debate. In 8th Interna- tional Conference on Language Resources and Eval- uation (LREC 2012), pages 812-817, Istanbul.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "A survey on emotional semantic image retrieval",
"authors": [
{
"first": "Weining",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Qianhua",
"middle": [],
"last": "He",
"suffix": ""
}
],
"year": 2008,
"venue": "International Conference on Image Processing",
"volume": "",
"issue": "",
"pages": "117--120",
"other_ids": {
"DOI": [
"10.1109/ICIP.2008.4711705"
]
},
"num": null,
"urls": [],
"raw_text": "Weining Wang and Qianhua He. 2008. A survey on emotional semantic image retrieval. In International Conference on Image Processing (ICIP 2008), pages 117-120. IEEE.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "David Cameron says UK will take thousands more Syrian refugees. The Guardian",
"authors": [
{
"first": "Nicholas",
"middle": [],
"last": "Watt",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nicholas Watt. 2015. David Cameron says UK will take thousands more Syrian refugees. The Guardian.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Recognizing contextual polarity in phraselevel sentiment analysis",
"authors": [
{
"first": "Theresa",
"middle": [],
"last": "Wilson",
"suffix": ""
},
{
"first": "Janyce",
"middle": [],
"last": "Wiebe",
"suffix": ""
},
{
"first": "Paul",
"middle": [],
"last": "Hoffmann",
"suffix": ""
}
],
"year": 2005,
"venue": "Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005)",
"volume": "",
"issue": "",
"pages": "347--354",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase- level sentiment analysis. In Human Language Technology Conference and Conference on Em- pirical Methods in Natural Language Processing (HLT/EMNLP 2005), pages 347-354. ACL.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Make America meme again: the rhetoric of the altright, volume 45 of Frontiers in political communication",
"authors": [
{
"first": "Suzanne",
"middle": [],
"last": "Heather",
"suffix": ""
},
{
"first": "Leslie",
"middle": [
"Ann"
],
"last": "Woods",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Hahner",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Heather Suzanne Woods and Leslie Ann Hahner. 2019. Make America meme again: the rhetoric of the alt- right, volume 45 of Frontiers in political communi- cation. Peter Lang, New York.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Learn more about what you see on google images",
"authors": [
{
"first": "Angela",
"middle": [],
"last": "Wu",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Angela Wu. 2020. Learn more about what you see on google images. Google Blog.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Image collector: An imagegathering system from the world-wide web employing keyword-based search engines",
"authors": [
{
"first": "Keiji",
"middle": [],
"last": "Yanai",
"suffix": ""
}
],
"year": 2001,
"venue": "International Conference on Multimedia and Expo",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.1109/ICME.2001.1237772"
]
},
"num": null,
"urls": [],
"raw_text": "Keiji Yanai. 2001. Image collector: An image- gathering system from the world-wide web employ- ing keyword-based search engines. In International Conference on Multimedia and Expo, (ICME 2001). IEEE.",
"links": null
},
"BIBREF42": {
"ref_id": "b42",
"title": "On the origins of memes by means of fringe web communities",
"authors": [
{
"first": "Savvas",
"middle": [],
"last": "Zannettou",
"suffix": ""
},
{
"first": "Tristan",
"middle": [],
"last": "Caulfield",
"suffix": ""
},
{
"first": "Jeremy",
"middle": [],
"last": "Blackburn",
"suffix": ""
},
{
"first": "Emiliano",
"middle": [],
"last": "De Cristofaro",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Sirivianos",
"suffix": ""
},
{
"first": "Gianluca",
"middle": [],
"last": "Stringhini",
"suffix": ""
},
{
"first": "Guillermo",
"middle": [],
"last": "Suarez-Tangil",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Internet Measurement Conference (IMC 2018)",
"volume": "",
"issue": "",
"pages": "188--202",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Savvas Zannettou, Tristan Caulfield, Jeremy Black- burn, Emiliano De Cristofaro, Michael Sirivianos, Gianluca Stringhini, and Guillermo Suarez-Tangil. 2018. On the origins of memes by means of fringe web communities. In Proceedings of the Internet Measurement Conference (IMC 2018), pages 188- 202. ACM.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"uris": null,
"num": null,
"type_str": "figure",
"text": "Example results for the query nuclear energy that are (a) off-topic, (b) not argumentative, (c) supportive, and (d) with ambiguous stance."
},
"FIGREF1": {
"uris": null,
"num": null,
"type_str": "figure",
"text": "Annotation interface, showing (from top to bottom) the current topic, question, and image, as well as the generic annotation options and comment box."
},
"FIGREF2": {
"uris": null,
"num": null,
"type_str": "figure",
"text": "Examples of images annotated as being able to support both stances but corrected to neither."
},
"FIGREF3": {
"uris": null,
"num": null,
"type_str": "figure",
"text": "Topic-wise analysis depicting Fleiss' \u03ba, precision@10 per relevance level averaged across expansion methods and stances, and the stance distribution for the retrieved images as judged by our annotators."
},
"FIGREF5": {
"uris": null,
"num": null,
"type_str": "figure",
"text": "Example images representing different categories of images that are able to support both stances: (a) contextual information: 70% of 111 analyzed images, (b) sources: 9%, (c) definitions: 10%, (d) key point tables: 2%, and (e) ambiguous messages: 9%."
},
"TABREF0": {
"html": null,
"text": "Search engine for argumentative images using stance-aware query expansion Search engine for argumentative images using keyword-based image search",
"content": "<table><tr><td>(a)</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>q</td><td>expansion Query</td><td>q 1 , ..., q n</td><td>Keyword-based image search</td><td>R 1 , ..., R n</td><td>Result classification and re-ranking</td><td>R R</td><td>+ \u2212</td><td>+ \u2212</td></tr><tr><td>(b)</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>q</td><td>Stance-aware query expansion</td><td>q 1 , ..., q n + + \u2212 \u2212 q 1 , ..., q m</td><td>Keyword-based image search Keyword-based</td><td>R 1 , ..., R n + + R 1 , ..., R m \u2212 \u2212</td><td>Result list interlacing Result list</td><td>R R</td><td>+ \u2212</td><td>+ \u2212</td></tr><tr><td/><td/><td/><td>image search</td><td/><td>interlacing</td><td/><td/><td/></tr></table>",
"num": null,
"type_str": "table"
}
}
}
} |