File size: 102,971 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 |
{
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T02:11:41.185873Z"
},
"title": "A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter",
"authors": [
{
"first": "Ishaan",
"middle": [],
"last": "Arora",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Columbia University",
"location": {}
},
"email": ""
},
{
"first": "Julia",
"middle": [],
"last": "Guo",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Columbia University",
"location": {}
},
"email": ""
},
{
"first": "Susan",
"middle": [
"E"
],
"last": "Mcgregor",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Columbia University",
"location": {}
},
"email": ""
},
{
"first": "Sarah",
"middle": [
"Ita"
],
"last": "Levitan",
"suffix": "",
"affiliation": {},
"email": "sarah.levitan@hunter.cuny.edu"
},
{
"first": "Julia",
"middle": [],
"last": "Hirschberg",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Columbia University",
"location": {}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators-not the targets of the abuse themselves. While this method of data collection supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by nontargets. Here, we present a mixed-methods approach to create classifiers for abuse and harassment which leverages direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We use women journalists' experience on Twitter as an initial community of focus. We identify several structural mechanisms of abuse that we believe will generalize to other target communities.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators-not the targets of the abuse themselves. While this method of data collection supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by nontargets. Here, we present a mixed-methods approach to create classifiers for abuse and harassment which leverages direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We use women journalists' experience on Twitter as an initial community of focus. We identify several structural mechanisms of abuse that we believe will generalize to other target communities.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Harassment is a significant problem in online spaces. In 2017, one in four Americans reported experiencing online harassment, with more than 60% describing it as a \"major problem\" (Duggan, 2017) .",
"cite_spans": [
{
"start": 180,
"end": 194,
"text": "(Duggan, 2017)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "For journalists, a social media presence is essentially a professional requirement, as it is both a mechanism for locating sources and for promoting stories (Ferrier and Garud-Patkar, 2018) ; as of 2018, more Americans (roughly 20%) get their news from social media than from printed newspapers (Shearer, 2018) . At the same time, journalists receive an inordinate volume of hateful and harassing messages via social media. In a recent survey conducted by the Committee to Protect Journalists (CPJ), 90% of American journalists described online harassment as the biggest threat facing journalists today, with women and minority journalists being disproportionately targeted online (Westcott and Foley, 2019) .",
"cite_spans": [
{
"start": 157,
"end": 189,
"text": "(Ferrier and Garud-Patkar, 2018)",
"ref_id": "BIBREF10"
},
{
"start": 295,
"end": 310,
"text": "(Shearer, 2018)",
"ref_id": null
},
{
"start": 681,
"end": 707,
"text": "(Westcott and Foley, 2019)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "This harassment can have devastating effects. In 2016, 10% of women journalists said that they had considered leaving the profession out of fear (Nilsson and\u00d6rnebring, 2016) , while others avoided certain coverage areas in an effort to mitigate the risk of harassment. Still others may choose not to enter the field at all.",
"cite_spans": [
{
"start": 145,
"end": 173,
"text": "(Nilsson and\u00d6rnebring, 2016)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "At a time when there is a major need to retain skilled journalists and diversify newsrooms (Scire, 2020), our goal is to develop a research methodology to address this critical threat facing journalists, and ultimately, our free press. Our contributions in this paper include: a) Identifying gaps in current anti-harassment tools provided by Twitter; b) Identifying key strategies used by harassers to circumvent these tools and reach their targets; and c) Development of a direct-engagement research process and data collection platform to curate datasets with high ecological validity, which will ultimately be used to train better machine learning classifiers for harassment detection.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Currently, there are limited options available for journalists to deal with harassing messages on Twitter. Twitter has three primary mechanisms through which a user can control their interactions on the platform: muting, blocking, and the recently introduced \"conversations\" controls, all of which have a slightly different impact on the content a user can access. For example, muting and blocking can both prevent content from certain users from appearing in some user A's timeline (Twitter, c) (Twitter, d).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation and Approach",
"sec_num": "2"
},
{
"text": "However, muted users can still follow and interact with A, while blocked users are no longer able to see A's tweets, and if they visit A's profile, they will see they have been blocked (Twitter, b). The new \"conversations\" feature, meanwhile, allows user A to specify whether everyone, everyone they follow, or only specific users can reply to a specific tweet (Twitter, a). While these tools offer impressive granularity, many journalists have both large followings and a professional mandate to interact with their audiences on social media. This makes many of the available controls impractical or ineffective. Moreover, two of the three tools Twitter offers are only effective retrospectively, meaning the targeted user must still read blocked users' offensive tweets before they can choose to mute or block them. Not only does this require journalists to experience harm in order to achieve any potential remediation, if they are targeted by a large number of accounts, the manual effort becomes time-prohibitive.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation and Approach",
"sec_num": "2"
},
{
"text": "Shared blocklists have been touted as a means for addressing some of these issues (Geiger, 2016) . However, for journalists this can result in blocking users who may be sharing legitimate critiques of their work (Jhaver et al., 2018) . As a whole, journalists as a community have expressed desire for more effective user engagement management tools (Saridou et al., 2019) .",
"cite_spans": [
{
"start": 82,
"end": 96,
"text": "(Geiger, 2016)",
"ref_id": "BIBREF12"
},
{
"start": 212,
"end": 233,
"text": "(Jhaver et al., 2018)",
"ref_id": "BIBREF17"
},
{
"start": 349,
"end": 371,
"text": "(Saridou et al., 2019)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation and Approach",
"sec_num": "2"
},
{
"text": "Furthermore, while many social media platforms do already have automated mechanisms for filtering harassment and hate speech, these are largely based on keyword matching, requiring manual creation with no guarantee of accuracy. Due to the large scale of problematic content on social media worldwide, manual efforts by moderators and filters have also been insufficient (Gerrard, 2018) .",
"cite_spans": [
{
"start": 370,
"end": 385,
"text": "(Gerrard, 2018)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation and Approach",
"sec_num": "2"
},
{
"text": "The goal of this work is, therefore, to contribute a robust, generalizable mixed-methods approach to constructing harassment training datasets with strong ecological validity, in order to support the development of truly effective classifiers for proactively identifying real-world abusive, harassing, and demeaning speech towards specific communities on Twitter.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation and Approach",
"sec_num": "2"
},
{
"text": "Working with journalists, we are collecting a large-scale corpus of personally-harassing messages they have received on Twitter, and have developed an easily-employed annotation method to label messages by degree of observed harassment. Using this data, we then build machine learning classifiers to distinguish between hateful, abusive and neutral tweets. Ultimately, we plan to integrate our trained models into a tool to help journalists navigate and avoid having to see these unwanted, harassing messages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Motivation and Approach",
"sec_num": "2"
},
{
"text": "Prior work on automatic detection of hateful and abusive speech toward journalists is limited. In (Charitidis et al., 2020) , researchers used a manually-validated seed set of journalism-related Twitter accounts to generate a list of target accounts across five languages. Using the Twitter API to conduct keyword-based searches, they then manually annotated hate vs. non-hate tweets. This yielded highly imbalanced corpora, with more \"hate\" than \"non-hate\" tweets for each language. Deep learning models trained on each language corpus achieved best macro-F1 scores over .80 for English, French and Greek but somewhat lower for Spanish and German.",
"cite_spans": [
{
"start": 98,
"end": 123,
"text": "(Charitidis et al., 2020)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "Other work has addressed the more general problem of automatic identification of hate speech and abusive language online. In (Waseem, 2016) , researchers found that crowd-sourced annotations performed poorly. This indicates the importance of expert annotators, which (Blackwell et al., 2017) situates specifically in terms of classifying harassment.",
"cite_spans": [
{
"start": 125,
"end": 139,
"text": "(Waseem, 2016)",
"ref_id": "BIBREF29"
},
{
"start": 267,
"end": 291,
"text": "(Blackwell et al., 2017)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "In (Warner and Hirschberg, 2012) , researchers using data from Yahoo and the American Jewish Congress found that anti-Semitic hate speech differed linguistically from speech that targeted other religious or ethnic groups, highlighting the need for a community-specific approach to studying hate speech. (Salem et al., 2016) used content from self-identified hate communities, instead of keywords from hand-coded speech or manually coded hate speech terms, as training data for their work on hate speech detection with some success. In (Nobata et al., 2016) , researchers studied abusive language in online user comments on news and finance forums using linguistic, syntactic, and distributed semantic features as well as lexicon-based features. Their dataset has been used to benchmark performance in hate speech detection, as has (Waseem and Hovy, 2016) . In (Kshirsagar et al., 2018) , researchers developed deep learning models for hate speech detection on Twitter, using transformed word embeddings to classify hate speech on three public datasets.",
"cite_spans": [
{
"start": 3,
"end": 32,
"text": "(Warner and Hirschberg, 2012)",
"ref_id": "BIBREF28"
},
{
"start": 535,
"end": 556,
"text": "(Nobata et al., 2016)",
"ref_id": "BIBREF23"
},
{
"start": 831,
"end": 854,
"text": "(Waseem and Hovy, 2016)",
"ref_id": "BIBREF31"
},
{
"start": 860,
"end": 885,
"text": "(Kshirsagar et al., 2018)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "Researchers in journalism have also used more qualitative methods to study abusive and hateful speech towards journalists. For example, UT Austin's School of Journalism published results from in-depth interviews with 75 female journalists describing how rampant online sexual harassment disrupts their ability to do their jobs (Chen et al., 2018) . The Committee to Protect Journalists reported similar findings in 2019 (Westcott and Foley, 2019) .",
"cite_spans": [
{
"start": 327,
"end": 346,
"text": "(Chen et al., 2018)",
"ref_id": "BIBREF5"
},
{
"start": 420,
"end": 446,
"text": "(Westcott and Foley, 2019)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "Finally, we note that developers have created tools (e.g. Twitter Block Chain (Wren, 2019) and the recently discontinued Block Together (Hoffman-Andrews, 2020) and the forthcoming Block Party app (Chou, 2020)) specifically designed to address the manual nature of Twitter's muting and blocking functions. While these efforts appear to address an important limitation of Twitter's current systems, they remain a reactive, rather than proactive, approach.",
"cite_spans": [
{
"start": 78,
"end": 90,
"text": "(Wren, 2019)",
"ref_id": "BIBREF33"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "Our proposed methodology for training data collection and annotation incorporates and improves on these approaches as follows: 1We conduct background interviews with our target community of women journalists in order to identify common heuristics used to carry out harassment on Twitter, in order to develop a more nuanced and balanced dataset for annotation; (2) Annotations are performed by the targets of harassment, guaranteeing a unique level of ecological validity; (3) Our approach takes an empowering rather than exploitative approach to the detection process, promoting harm reduction by allowing harassment targets to participate constructively in the creation of classifiers that can better support their needs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "We employ a mixed-methods approach that integrates qualitative and quantitative data collection and analysis. We begin by directly engaging with our target group of women journalists who have experienced online harassment. We recruit participants by circulating calls to participation in key networks of women journalists, followed by semistructured pilot interviews with select participants, in which we question them about patterns of harassment that they have experienced or observed, and about potential tools or interventions that would improve their experience on social media. Despite our convenience sample, two key themes emerged across several pilot interviews, providing valuable insights about the mechanisms of harassment on Twitter, which we describe in Section 5.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methodology",
"sec_num": "4"
},
{
"text": "Results of these interviews are then integrated into our quantitative data collection pipeline. Using patterns of harassing language and behaviors on Twitter described by interview participants, we develop computational methods to automatically identify those patterns and then use these methods to sample potentially hateful messages from participants' Twitter archives for them to annotate. We describe this data selection process in Section 6.1. Through the process of direct engagement with our target community, we are able to curate a high quality dataset of labeled tweets to support the development of more robust harassment classifiers.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methodology",
"sec_num": "4"
},
{
"text": "To generate a well-balanced training set of tweets, we conducted pilot interviews with several women journalists who have faced significant harassment on Twitter. Through these interviews we learned about specific forms of the \"sub-tweeting\" and \"snitch-tweeting\" heuristics that are used to target these and other women journalists with abusive and harassing messages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Pilot Interviews",
"sec_num": "5"
},
{
"text": "The primary form of \"sub-tweeting\" described to us consists of perpetrators capturing screenshots that contain the target's Twitter profile or username. They then tweet these out with implicit or explicit calls for their followers to tweet at the same target. This behavior constitutes \"sub-tweeting\" because the absence of the target's username in the text of the original tweet means that target will not be notified of the instigating tweet, and will therefore be caught off-guard by an influx of often abusive tweets, sometimes numbering in the thousands over a period of less than a day. (See (Tufekci, 2014) for more details and examples of \"sub-tweeting.\") We note that none of Twitter's currently available tools can mitigate this attack; even if the perpetrator has already been blocked by the target, they can simply log out of Twitter and view the target's profile in a web browser in order to obtain the required media.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Pilot Interviews",
"sec_num": "5"
},
{
"text": "While the effect of sub-tweeting is to mask the identity of the perpetrator, \"snitch-tweeting\" is a means of drawing the target into a sub-tweeted thread about themselves to expose them to abuse. Because sub-tweeting intentionally circumvents Twitter's notification systems, targets of abuse will typically be unaware of such sub-tweeting, unless, as described above, it is used to direct traffic to their account. \"Snitch-tweeting\" consists of adding a target's handle to a thread about them, thus triggering a notification. The goal is for the target then to review the notification and thus to view the abusive thread that precedes the snitch-tweet. Taken together, these results helped us inform our design for the tweet selection portion of our data pre-processing, as described below.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Pilot Interviews",
"sec_num": "5"
},
{
"text": "In order to curate a high-quality training dataset from participating journalists' tweets, we designed and implemented a two-part, web-based platform to facilitate the data collection and annotation processes. This web platform was designed to balance the proportion of abusive vs. non-abusive tweets that are presented for annotation, without relying on keywords, which are often too coarsegrained to serve as a reliable indicator of abusive content. Instead, we develop heuristics using insights from our pilot interviews as well as private data from the participant's account to include a more nuanced and representative range of potentially abusive tweets for annotation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Design",
"sec_num": "6"
},
{
"text": "The platform is also designed to maximize the efficiency and accuracy of the annotation process, in order to generate a large volume of high-quality training data for deep learning models. We achieve this via batched contextual annotation: participants annotate tweets within the context of the original conversation or tweet thread, rather than annotating them in isolation, simulating how they would have viewed the conversation initially on Twitter. In addition to the annotation tool described above, we have also built a tool for secure data upload, as described below.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Design",
"sec_num": "6"
},
{
"text": "The process of using our web annotation tool is split into 2 stages, each of which can be accessed via secure, password-protected URLs. First, the study participant securely logs in to the upload platform using a uniquely generated username and password. We ask participants to upload three distinct files, which can be extracted from their Twitter data archive: (1) tweet.js, which contains all of their tweets; (2) muted.js, which contains the list of accounts they have muted, and (3) blocked.js, which contains the list of accounts they have blocked. Because participants' Twitter archives may contain anywhere from hundreds to tens of thousands of tweets, asking them to label all tweet threads is impractical. Moreover, our goal is to build a training corpus that is approximately equally split between hateful/abusive examples and neutral examples -a very different distribution than we expect to see across the entire corpus, making random sampling inefficient for these purposes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Structure",
"sec_num": "6.1"
},
{
"text": "In order to capture more varied and nuanced examples of problematic data than are likely to be generated by common techniques like keyword filtering, we use multiple heuristics inspired by the participant's muted and blocked lists and the insights gained from our pilot studies to curate a manageable sample of tweets for annotation. Applying these heuristics involves a combination of manual and scripted processing, resulting a gap of several hours to one day between data upload and the availability of data for annotation by each participant. A list of balanced tweet threads fetched from both of these heuristics described below is used to populate the annotation interface.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Structure",
"sec_num": "6.1"
},
{
"text": "Our first heuristic using muted and blocked lists uses a Python script to identify all tweets in the tweet.js file that contain any username present in either the muted.js or blocked.js files. Because the presence of a username in these lists reflects an intentional choice on the part of the participant to have these accounts' tweets hidden or blocked from their timeline, we believe the proportion of harmful tweets involving these usernames is likely to be higher than what is present in the corpus as a whole. We then use the thread-retrieval algorithm described in Section 6.1.1 to construct the thread for each relevant tweet.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Structure",
"sec_num": "6.1"
},
{
"text": "Our second heuristic searches sub-tweets (described in 5) targeting the study participant, using the query \"[real name] -from:[username] -@[username]\" where \"username\" is the participant's Twitter handle, and \"real name\" is the participant's real name. This method allows us to find and capture Tweets in which the study participant was \"sub-tweeted\" over the most recent 30 days (using Twitter's non-premium Search API). Each of these tweets is then passed through the procedure in Algorithm 1 to once again obtain the corresponding tweet threads.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Structure",
"sec_num": "6.1"
},
{
"text": "We find that this methodology retrieves a few interesting threads, but has several shortcomings. First, many of these tweets are positive, and praise the journalist for their work, which makes sense as their name is directly mentioned. Second, and relatedly, we are unable to find sub-tweets where the journalist's name is not mentioned, i.e. the post merely consists of a screenshot of their tweet. These tweets are presumably more negative, as they avoid easy attention from the target. In order to find these sub-tweets, we would have to implement computer vision methods to search for their name in images across Twitter, though it could be difficult to know where to look for these screenshots in the first place. We will investigate this further in future work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Structure",
"sec_num": "6.1"
},
{
"text": "We have also attempted to build a third heuristic using the study participant's Twitter archive to capture scenarios where they had been \"snitch-Tweeted\" into one of these sub-tweet threads, i.e. find a thread of the structure [image, ..., mention of their username, their response], but we did not find any such threads. We plan to revisit this with future annotators.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Structure",
"sec_num": "6.1"
},
{
"text": "To balance the potentially negative threads identified through these heuristics, we also select a random sample of tweets made to non-blocked, non-muted users, and retrieve their corresponding threads. We also exclude from this non-negative sample tweet threads constructed by the participant through self-replies.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Platform Structure",
"sec_num": "6.1"
},
{
"text": "After data upload and preprocessing, the annotation platform is deployed and sent to the study participant. Participants annotate each tweet sent to them within a retrieved tweet thread. This provides better context to the participant while annotating, addressing a key limitation of many existing datasets, where tweets are presented without context.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Annotation Platform",
"sec_num": "6.1.1"
},
{
"text": "Algorithm 1 presents pseudocode for computing a tweet thread from a given tweet. To see the full codebase which joins this algorithm with the aforementioned heuristics into a complete data processing pipeline, please refer to the GitHub repository linked below. \u2022 Hateful speech is defined as language used to express hatred towards a targeted individual or group, or which is intended to be derogatory, to humiliate, or to insult members of the group, on the basis of attributes such as race, religion, ethnic origin, sexual orientation, disability, or gender.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Annotation Platform",
"sec_num": "6.1.1"
},
{
"text": "\u2022 Abusive language is defined as any strongly impolite, rude or hurtful language using profanity, that debases someone or something, or shows intense negative emotion.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Annotation Platform",
"sec_num": "6.1.1"
},
{
"text": "\u2022 Spam includes posts consisting of related or unrelated advertising / marketing, selling products of adult nature, linking to malicious websites, phishing attempts and other kinds of unwanted information, usually executed repeatedly.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Annotation Platform",
"sec_num": "6.1.1"
},
{
"text": "\u2022 Neutral is all tweets that do not fall into any of the prior categories.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Annotation Platform",
"sec_num": "6.1.1"
},
{
"text": "We drew these labels from (Founta et al., 2018) 's work, which created a hate speech dataset of 80,000 tweets labeled by crowdsourced annotators, using several iterations of labels (including \"offensive\", \"aggressive\", etc.), narrowing them down to these terms. We plan to further iteratively add and remove labels based on insights from interviews and annotation sessions (see 8).",
"cite_spans": [
{
"start": 26,
"end": 47,
"text": "(Founta et al., 2018)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Annotation Platform",
"sec_num": "6.1.1"
},
{
"text": "While we are recruiting more journalists as study participants into our data collection pipeline, we have in parallel been building models of both feature engineering and neural network-based approaches, and testing them on historical hate speech datasets. We plan to take the insights we acquire from these experiments and apply them to classifiers built on our own data once we have accumulated a sufficient amount. We also plan to check the cross-performance between models trained on our own and historical corpora as quality assurance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Modeling",
"sec_num": "7"
},
{
"text": "The data which we have accumulated so far gives us a good idea of which historical corpora are most similar to our own. We explored several corpora, including (Waseem and Hovy, 2016) and (Founta et al., 2018) , but focused on Task 5 of SemEval 2019, \"Multilingual detection of hate speech against immigrants and women in Twitter (HatEval)\" in English (Basile et al., 2019) , as it is most recent and they are all of similar genre.",
"cite_spans": [
{
"start": 159,
"end": 182,
"text": "(Waseem and Hovy, 2016)",
"ref_id": "BIBREF31"
},
{
"start": 187,
"end": 208,
"text": "(Founta et al., 2018)",
"ref_id": "BIBREF11"
},
{
"start": 351,
"end": 372,
"text": "(Basile et al., 2019)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Modeling",
"sec_num": "7"
},
{
"text": "Both Task 5 subtasks used the same dataset (cicl2018/HateEvalTeam, 2019) but with different labels. Subtask A was a binary classification task to assign a label of \"hate\" or \"non-hate\" to each tweet. Subtask B was a multi-class classification task to assign two additional label pairs to each tweet in addition to \"hate\" or \"non-hate\": \"individual\" or \"group\" and \"aggressive\" or \"non-aggressive\". The split across train and development datasets was 9000 to 1000 tweets; these have been open-sourced by the organizing team. The true labels for the test set have not, however, so we evaluate only on the development set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Modeling",
"sec_num": "7"
},
{
"text": "We replicated the winning approach (Indurthi et al., 2019) for sub-task A in English, which used SMOTE to over-sample the \"hate\" class as a preprocessing step, followed by the use of Universal Sentence Encoder (Cer et al., 2018) to generate a vector representation of the tweet, and SVM (RBF kernel) to classify the tweet. We also implemented a transformer-based approach for this subtask, based on (MacAvaney et al., 2019) , which uses pre-trained BERT for sequence classification, fine-tuned for 10 epochs. This approach in fact outperforms the aforementioned winning approach.",
"cite_spans": [
{
"start": 35,
"end": 58,
"text": "(Indurthi et al., 2019)",
"ref_id": "BIBREF16"
},
{
"start": 210,
"end": 228,
"text": "(Cer et al., 2018)",
"ref_id": "BIBREF3"
},
{
"start": 399,
"end": 423,
"text": "(MacAvaney et al., 2019)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Modeling",
"sec_num": "7"
},
{
"text": "For sub-task B, the multi-classification task, we replicated the winning approach (Bauwelinck et al., 2019) by training three separate classifiers to classify three label pairs individually; these classifiers used a linear SVM on handcrafted syntactic, lexical and bag-of-words features. The optimal hyperparameters were found using grid search. Our experiments with these corpora have given us insights about best practices for training effective models of hate speech, which we plan to apply to our new corpus as we collect more data from participating women journalists. We have additionally been exploring experiments on our collected data with various novel model architectures as opposed to data corpora, which are elaborated upon in 10.",
"cite_spans": [
{
"start": 82,
"end": 107,
"text": "(Bauwelinck et al., 2019)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Modeling",
"sec_num": "7"
},
{
"text": "Although testing of our platform is still in the pilot phase, early users have shared positive feedback regarding its usability, and have also been able to perform the annotation task with good efficiency, on the order of \u223c300 tweets per hour. Given the size of previously-collected datasets in this space, our methodology is efficient enough to generate sufficient training data in less than 40 hours, making it both a cost-effective and robust approach. Given the high fidelity of our labels and the nearperfect ecological validity of the training data, we believe that classifiers trained on data collected using our methods will significantly outperform existing classifiers on hateful and abusive speech in the wild.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Discussion",
"sec_num": "8"
},
{
"text": "From early feedback, we have also identified additional labels that participants found relevant, such as \"campaign\" or \"brigade\", used to indicate a lexically generic Tweet that is still part of a harassment campaign, as in 2019's \"Learn to code\" campaign (Molloy, 2019) . In addition, our pilot interviews suggest that including a fill-in \"other\" label may be useful for generating more nuanced classifiers, especially as there has historically been a lack of annotator agreement on what constitutes hateful speech, which tends to vary in severity and lexical nature depending on the situation (Waseem et al., 2017) .",
"cite_spans": [
{
"start": 256,
"end": 270,
"text": "(Molloy, 2019)",
"ref_id": "BIBREF21"
},
{
"start": 595,
"end": 616,
"text": "(Waseem et al., 2017)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Discussion",
"sec_num": "8"
},
{
"text": "Currently, our approach is limited by its dependence on a feature allowing Twitter users to download an archive of their data; this feature was suspended for roughly two months of the research period in response to the social-engineered hacking of more than 100 accounts (Conger and Popper, 2020) . Moreover, some blocked or muted users identified in pre-processing may have been suspended by Twitter, making it impossible to include their potentially harassing messages in our corpus. Finally, while our platform yielded a useful annotation rate, we note that there are inherent limitations to developing classifiers using strictly hand-labeled data.",
"cite_spans": [
{
"start": 271,
"end": 296,
"text": "(Conger and Popper, 2020)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Limitations",
"sec_num": "9"
},
{
"text": "Given the interruption in data collection, we propose to augment our data-access pipeline by building a sufficiently-permissioned Twitter app to download the required data directly from participants' accounts. This would not only provide similarly high-quality data with less burden on participants, it would also provide an ongoing source of test data with which we could refine and improve our classifiers in much closer to real-time.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Directions for Future Work",
"sec_num": "10"
},
{
"text": "By leveraging the methods presented in (Wulczyn et al., 2017) , moreover, we also believe we could augment and improve the classifiers built from our hand-labeled data using a combination of machine learning and crowdsourcing. We are in general investigating ways to overcome the inherent shortcomings of manual expert annotation, while retaining its significant benefits; for example, augmenting our data annotation tool with active learning annotation (Vlachos, 2006) , so that participants only need to annotate the most unclear instances of hateful/harassing/neutral speech.",
"cite_spans": [
{
"start": 39,
"end": 61,
"text": "(Wulczyn et al., 2017)",
"ref_id": "BIBREF34"
},
{
"start": 454,
"end": 469,
"text": "(Vlachos, 2006)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Directions for Future Work",
"sec_num": "10"
},
{
"text": "In regards to model-building, we are exploring ways we can take advantage of the contextual thread annotation scheme present in our annotation platform. Specifically, we have investigated methods using LSTMs (Huang et al., 2016) , and are presently investigating graph attention networks (Veli\u010dkovi\u0107 et al., 2017) ; these architectures and others like them could allow us to take advantage of the rich metadata and parent tweet text embeddings present in tweet threads, and have the potential to achieve significantly boosted classification performance compared to that of models built on text embeddings of the potentially harassing tweet alone (Mishra et al., 2019) .",
"cite_spans": [
{
"start": 208,
"end": 228,
"text": "(Huang et al., 2016)",
"ref_id": "BIBREF15"
},
{
"start": 288,
"end": 313,
"text": "(Veli\u010dkovi\u0107 et al., 2017)",
"ref_id": "BIBREF26"
},
{
"start": 646,
"end": 667,
"text": "(Mishra et al., 2019)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Directions for Future Work",
"sec_num": "10"
},
{
"text": "For the purpose of building the eventual tool to aid journalists in the field, we could alternatively address the relatively small size of our manuallylabelled datasets for training deep learning classifiers, by augmenting them against the large, popular corpora already in existence. We could investigate whether this addition would boost performance compared to classifiers trained only on those large, crowd-sourced corpora, as a measure of effectiveness of our methodology.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Directions for Future Work",
"sec_num": "10"
},
{
"text": "Finally, we note that while certain semantic features of the classifiers developed using our methodology will differ depending on the community of focus, we hypothesize that by studying several communities with this level of detail and quality, we will eventually be able to identify generalizable features of harassment activities.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Directions for Future Work",
"sec_num": "10"
},
{
"text": "This work has focused on outlining a novel and generalizable methodology for generating better training datasets for the detection of abusive and harassing speech on Twitter, using women journalists as a test community. By directly engaging the targets of harassment in our research, we have not only created an efficient annotation platform using insights about the structural mechanisms of harassment, but we have offered these victims a constructive way to engage with what are otherwise totally negative experiences. We look forward to continuing to work with women journalists to build data-driven tools against abuse and harassment that allow them to maintain their personal needs while working to uphold our free press.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "11"
},
{
"text": "The code for all of our tweet filtering heuristics and thread retrieval methods can be accessed at the following GitHub repository: https://github.com/ ishaan007/woah_emnlp_2020",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "Theodora Saridou, Kosmas Panagiotidis, and Andreas Veglis. 2019. Towards a semantic-oriented model of participatory journalism management: Perceptions of user-generated content. Redefining Communication: Social Media and the Age of Innovation, page 27.Sarah Scire. 2020. A window into one newsroom's diversity opens, but an industrywide door shuts (for now).NiemanLab.https://www.niemanlab.org/2020/05/awindow-into-one-newsrooms-diversityopens-but-an-industry-wide-door-shutsfor-now.Elisa Shearer. 2018. Social media outpaces print newspapers in the u.s. as a news source.https://www.pewresearch.org/facttank/2018/12/10/social-media-outpacesprint-newspapers-in-the-u-s-as-anews-source/.Zeynep Tufekci. 2014. Big questions for social media big data: Representativeness, validity and other methodological pitfalls. arXiv preprint arXiv:1403.7400.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "annex",
"sec_num": null
},
{
"text": "About conversations on twitter.https://help.twitter.com/en/usingtwitter/twitter-conversations.Twitter. b. How to block accounts on twitter.https://help.twitter.com/en/usingtwitter/blocking-and-unblockingaccounts.Twitter. c. How to mute accounts on twitter. https://help.twitter.com/en/usingtwitter/twitter-mute.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Twitter. a.",
"sec_num": null
},
{
"text": "How to use advanced muting options. https://help.twitter.com/en/using-",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Twitter. d.",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter",
"authors": [
{
"first": "Valerio",
"middle": [],
"last": "Basile",
"suffix": ""
},
{
"first": "Cristina",
"middle": [],
"last": "Bosco",
"suffix": ""
},
{
"first": "Elisabetta",
"middle": [],
"last": "Fersini",
"suffix": ""
},
{
"first": "Debora",
"middle": [],
"last": "Nozza",
"suffix": ""
},
{
"first": "Viviana",
"middle": [],
"last": "Patti",
"suffix": ""
},
{
"first": "Francisco Manuel Rangel",
"middle": [],
"last": "Pardo",
"suffix": ""
},
{
"first": "Paolo",
"middle": [],
"last": "Rosso",
"suffix": ""
},
{
"first": "Manuela",
"middle": [],
"last": "Sanguinetti",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 13th International Workshop on Semantic Evaluation",
"volume": "",
"issue": "",
"pages": "54--63",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela San- guinetti. 2019. SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In Proceedings of the 13th Inter- national Workshop on Semantic Evaluation, pages 54-63, Minneapolis, Minnesota, USA.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "LT3 at SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter (hatEval)",
"authors": [
{
"first": "Nina",
"middle": [],
"last": "Bauwelinck",
"suffix": ""
},
{
"first": "Gilles",
"middle": [],
"last": "Jacobs",
"suffix": ""
},
{
"first": "V\u00e9ronique",
"middle": [],
"last": "Hoste",
"suffix": ""
},
{
"first": "Els",
"middle": [],
"last": "Lefever",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 13th International Workshop on Semantic Evaluation",
"volume": "",
"issue": "",
"pages": "436--440",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nina Bauwelinck, Gilles Jacobs, V\u00e9ronique Hoste, and Els Lefever. 2019. LT3 at SemEval-2019 task 5: Multilingual detection of hate speech against immi- grants and women in twitter (hatEval). In Proceed- ings of the 13th International Workshop on Seman- tic Evaluation, pages 436-440, Minneapolis, Min- nesota, USA. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Classification and its consequences for online harassment: Design insights from heartmob",
"authors": [
{
"first": "Lindsay",
"middle": [],
"last": "Blackwell",
"suffix": ""
},
{
"first": "Jill",
"middle": [],
"last": "Dimond",
"suffix": ""
},
{
"first": "Sarita",
"middle": [],
"last": "Schoenebeck",
"suffix": ""
},
{
"first": "Cliff",
"middle": [],
"last": "Lampe",
"suffix": ""
}
],
"year": 2017,
"venue": "Proc. ACM Hum.-Comput. Interact",
"volume": "1",
"issue": "24",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lindsay Blackwell, Jill Dimond, Sarita Schoenebeck, and Cliff Lampe. 2017. Classification and its con- sequences for online harassment: Design insights from heartmob. Proc. ACM Hum.-Comput. Inter- act., 1(24):19pp.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Universal sentence encoder",
"authors": [
{
"first": "Daniel",
"middle": [],
"last": "Cer",
"suffix": ""
},
{
"first": "Yinfei",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Sheng-Yi",
"middle": [],
"last": "Kong",
"suffix": ""
},
{
"first": "Nan",
"middle": [],
"last": "Hua",
"suffix": ""
},
{
"first": "Nicole",
"middle": [],
"last": "Limtiaco",
"suffix": ""
},
{
"first": "Rhomni",
"middle": [],
"last": "St John",
"suffix": ""
},
{
"first": "Noah",
"middle": [],
"last": "Constant",
"suffix": ""
},
{
"first": "Mario",
"middle": [],
"last": "Guajardo-Cespedes",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Yuan",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Tar",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1803.11175"
]
},
"num": null,
"urls": [],
"raw_text": "Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al. 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Stavros Vologiannidis, Ioannis Papastergiou, and Sophia Karakeva. 2020. Towards countering hate speech against journalists on social media",
"authors": [
{
"first": "Polychronis",
"middle": [],
"last": "Charitidis",
"suffix": ""
},
{
"first": "Stavros",
"middle": [],
"last": "Doropoulos",
"suffix": ""
}
],
"year": null,
"venue": "Online Social Networks and Media",
"volume": "17",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Polychronis Charitidis, Stavros Doropoulos, Stavros Vologiannidis, Ioannis Papastergiou, and Sophia Karakeva. 2020. Towards countering hate speech against journalists on social media. Online Social Networks and Media, 17:100071.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "you really have to have a thick skin': A cross-cultural perspective on how online harassment influences female journalists",
"authors": [
{
"first": "Gina",
"middle": [],
"last": "Masullo Chen",
"suffix": ""
},
{
"first": "Paromita",
"middle": [],
"last": "Pain",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Victoria",
"suffix": ""
},
{
"first": "Madlin",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Nina",
"middle": [],
"last": "Mekelburg",
"suffix": ""
},
{
"first": "Franziska",
"middle": [],
"last": "Springer",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Troger",
"suffix": ""
}
],
"year": 2018,
"venue": "Journalism",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gina Masullo Chen, Paromita Pain, Victoria Y Chen, Madlin Mekelburg, Nina Springer, and Franziska Troger. 2018. 'you really have to have a thick skin': A cross-cultural perspective on how online harassment influences female journalists. Journal- ism, page 1464884918768500.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "HateEval 2019 Task 5 Data Files",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "cicl2018/HateEvalTeam. 2019. HateEval 2019 Task 5 Data Files. https://github.com/cicl2018/ HateEvalTeam/tree/master/Data%20Files/ Data%20Files.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Florida teenager is charged as 'mastermind' of twitter hack. The New York Times",
"authors": [
{
"first": "Kate",
"middle": [],
"last": "Conger",
"suffix": ""
},
{
"first": "Nathanial",
"middle": [],
"last": "Popper",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kate Conger and Nathanial Popper. 2020. Florida teenager is charged as 'mastermind' of twitter hack. The New York Times.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Trollbusters: Fighting online harassment of women journalists",
"authors": [
{
"first": "Michelle",
"middle": [],
"last": "Ferrier",
"suffix": ""
},
{
"first": "Nisha",
"middle": [],
"last": "Garud-Patkar",
"suffix": ""
}
],
"year": 2018,
"venue": "Mediating Misogyny: Gender, Technology, and Harassment",
"volume": "",
"issue": "",
"pages": "311--332",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michelle Ferrier and Nisha Garud-Patkar. 2018. Troll- busters: Fighting online harassment of women jour- nalists. In Jacqueline Ryan Vickery and Tracy Ever- bach, editors, Mediating Misogyny: Gender, Tech- nology, and Harassment, pages 311-332. Springer International Publishing.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Large scale crowdsourcing and characterization of twitter abusive behavior",
"authors": [
{
"first": "Antigoni-Maria",
"middle": [],
"last": "Founta",
"suffix": ""
},
{
"first": "Constantinos",
"middle": [],
"last": "Djouvas",
"suffix": ""
},
{
"first": "Despoina",
"middle": [],
"last": "Chatzakou",
"suffix": ""
},
{
"first": "Ilias",
"middle": [],
"last": "Leontiadis",
"suffix": ""
},
{
"first": "Jeremy",
"middle": [],
"last": "Blackburn",
"suffix": ""
},
{
"first": "Gianluca",
"middle": [],
"last": "Stringhini",
"suffix": ""
},
{
"first": "Athena",
"middle": [],
"last": "Vakali",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Sirivianos",
"suffix": ""
},
{
"first": "Nicolas",
"middle": [],
"last": "Kourtellis",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1802.00393"
]
},
"num": null,
"urls": [],
"raw_text": "Antigoni-Maria Founta, Constantinos Djouvas, De- spoina Chatzakou, Ilias Leontiadis, Jeremy Black- burn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018. Large scale crowdsourcing and characterization of twitter abusive behavior. arXiv preprint arXiv:1802.00393.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Bot-based collective blocklists in twitter: the counterpublic moderation of harassment in a networked public space",
"authors": [
{
"first": "R",
"middle": [],
"last": "",
"suffix": ""
},
{
"first": "Stuart",
"middle": [],
"last": "Geiger",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "19",
"issue": "",
"pages": "787--803",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. Stuart Geiger. 2016. Bot-based collective block- lists in twitter: the counterpublic moderation of ha- rassment in a networked public space. Information, Communication & Society, 19(6):787-803.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Beyond the hashtag: Circumventing content moderation on social media",
"authors": [
{
"first": "Ysabel",
"middle": [],
"last": "Gerrard",
"suffix": ""
}
],
"year": 2018,
"venue": "New Media & Society",
"volume": "20",
"issue": "12",
"pages": "4492--4511",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ysabel Gerrard. 2018. Beyond the hashtag: Circum- venting content moderation on social media. New Media & Society, 20(12):4492-4511.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Block together",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Hoffman-Andrews",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jacob Hoffman-Andrews. 2020. Block together.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Modeling rich contexts for sentiment classification with lstm",
"authors": [
{
"first": "Minlie",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Yujie",
"middle": [],
"last": "Cao",
"suffix": ""
},
{
"first": "Chao",
"middle": [],
"last": "Dong",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1605.01478"
]
},
"num": null,
"urls": [],
"raw_text": "Minlie Huang, Yujie Cao, and Chao Dong. 2016. Mod- eling rich contexts for sentiment classification with lstm. arXiv preprint arXiv:1605.01478.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "FERMI at SemEval-2019 task 5: Using sentence embeddings to identify hate speech against immigrants and women in twitter",
"authors": [
{
"first": "Vijayasaradhi",
"middle": [],
"last": "Indurthi",
"suffix": ""
},
{
"first": "Bakhtiyar",
"middle": [],
"last": "Syed",
"suffix": ""
},
{
"first": "Manish",
"middle": [],
"last": "Shrivastava",
"suffix": ""
},
{
"first": "Nikhil",
"middle": [],
"last": "Chakravartula",
"suffix": ""
},
{
"first": "Manish",
"middle": [],
"last": "Gupta",
"suffix": ""
},
{
"first": "Vasudeva",
"middle": [],
"last": "Varma",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 13th International Workshop on Semantic Evaluation",
"volume": "",
"issue": "",
"pages": "70--74",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vijayasaradhi Indurthi, Bakhtiyar Syed, Manish Shri- vastava, Nikhil Chakravartula, Manish Gupta, and Vasudeva Varma. 2019. FERMI at SemEval-2019 task 5: Using sentence embeddings to identify hate speech against immigrants and women in twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 70-74, Minneapolis, Minnesota, USA. Association for Computational Linguistics.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Online harassment and content moderation: The case of blocklists",
"authors": [
{
"first": "Shagun",
"middle": [],
"last": "Jhaver",
"suffix": ""
},
{
"first": "Sucheta",
"middle": [],
"last": "Ghoshal",
"suffix": ""
},
{
"first": "Amy",
"middle": [],
"last": "Bruckman",
"suffix": ""
},
{
"first": "Eric",
"middle": [],
"last": "Gilbert",
"suffix": ""
}
],
"year": 2018,
"venue": "ACM Trans. Comput.-Hum. Interact",
"volume": "25",
"issue": "2",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shagun Jhaver, Sucheta Ghoshal, Amy Bruckman, and Eric Gilbert. 2018. Online harassment and con- tent moderation: The case of blocklists. ACM Trans. Comput.-Hum. Interact., 25(2):33pp.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Predictive embeddings for hate speech detection on twitter",
"authors": [
{
"first": "Rohan",
"middle": [],
"last": "Kshirsagar",
"suffix": ""
},
{
"first": "Tyrus",
"middle": [],
"last": "Cukuvac",
"suffix": ""
},
{
"first": "Kathleen",
"middle": [],
"last": "Mckeown",
"suffix": ""
},
{
"first": "Susan",
"middle": [],
"last": "Mcgregor",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)",
"volume": "",
"issue": "",
"pages": "26--32",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rohan Kshirsagar, Tyrus Cukuvac, Kathleen McKe- own, and Susan McGregor. 2018. Predictive embed- dings for hate speech detection on twitter. In Pro- ceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 26-32.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Hate speech detection: Challenges and solutions",
"authors": [
{
"first": "Sean",
"middle": [],
"last": "Macavaney",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Hao-Ren",
"suffix": ""
},
{
"first": "Eugene",
"middle": [],
"last": "Yao",
"suffix": ""
},
{
"first": "Katina",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Nazli",
"middle": [],
"last": "Russell",
"suffix": ""
},
{
"first": "Ophir",
"middle": [],
"last": "Goharian",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Frieder",
"suffix": ""
}
],
"year": 2019,
"venue": "PLOS ONE",
"volume": "14",
"issue": "",
"pages": "1--16",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sean MacAvaney, Hao-Ren Yao, Eugene Yang, Katina Russell, Nazli Goharian, and Ophir Frieder. 2019. Hate speech detection: Challenges and solutions. PLOS ONE, 14:1-16.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Abusive language detection with graph convolutional networks",
"authors": [
{
"first": "Pushkar",
"middle": [],
"last": "Mishra",
"suffix": ""
},
{
"first": "Marco",
"middle": [
"Del"
],
"last": "Tredici",
"suffix": ""
},
{
"first": "Helen",
"middle": [],
"last": "Yannakoudakis",
"suffix": ""
},
{
"first": "Ekaterina",
"middle": [],
"last": "Shutova",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1904.04073"
]
},
"num": null,
"urls": [],
"raw_text": "Pushkar Mishra, Marco Del Tredici, Helen Yan- nakoudakis, and Ekaterina Shutova. 2019. Abu- sive language detection with graph convolutional networks. arXiv preprint arXiv:1904.04073.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "How a myth about journalists telling miners to \"learn to code\" helped people justify harassment",
"authors": [
{
"first": "Parker",
"middle": [],
"last": "Molloy",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Parker Molloy. 2019. How a myth about jour- nalists telling miners to \"learn to code\" helped people justify harassment. Media Matters.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Abusive language detection in online user content",
"authors": [
{
"first": "Chikashi",
"middle": [],
"last": "Nobata",
"suffix": ""
},
{
"first": "Joel",
"middle": [],
"last": "Tetreault",
"suffix": ""
},
{
"first": "Achint",
"middle": [],
"last": "Thomas",
"suffix": ""
},
{
"first": "Yashar",
"middle": [],
"last": "Mehdad",
"suffix": ""
},
{
"first": "Yi",
"middle": [],
"last": "Chang",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 25th international conference on world wide web",
"volume": "",
"issue": "",
"pages": "145--153",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016. Abusive lan- guage detection in online user content. In Proceed- ings of the 25th international conference on world wide web, pages 145-153.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "A web of hate: Tackling hateful speech in online social spaces",
"authors": [
{
"first": "Mohammad",
"middle": [],
"last": "Haji",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Salem",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Kelly",
"suffix": ""
},
{
"first": "Susan",
"middle": [],
"last": "Dillon",
"suffix": ""
},
{
"first": "Derek",
"middle": [],
"last": "Benesch",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ruths",
"suffix": ""
}
],
"year": 2016,
"venue": "the International Conference on Language Resources and Evaluation (LREC2016)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Haji Mohammad Salem, Kelly P Dillon, Susan Be- nesch, and Derek Ruths. 2016. A web of hate: Tack- ling hateful speech in online social spaces. In First Workshop on Text Analytics for Cybersecurity and Online Safety (TA-COS 2016) at the International Conference on Language Resources and Evaluation (LREC2016).",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "twitter/advanced-twitter-mute-options",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "twitter/advanced-twitter-mute-options.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Graph attention networks",
"authors": [
{
"first": "Petar",
"middle": [],
"last": "Veli\u010dkovi\u0107",
"suffix": ""
},
{
"first": "Guillem",
"middle": [],
"last": "Cucurull",
"suffix": ""
},
{
"first": "Arantxa",
"middle": [],
"last": "Casanova",
"suffix": ""
},
{
"first": "Adriana",
"middle": [],
"last": "Romero",
"suffix": ""
},
{
"first": "Pietro",
"middle": [],
"last": "Lio",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1710.10903"
]
},
"num": null,
"urls": [],
"raw_text": "Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Active annotation",
"authors": [
{
"first": "Andreas",
"middle": [],
"last": "Vlachos",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Workshop on Adaptive Text Extraction and Mining",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Andreas Vlachos. 2006. Active annotation. In Pro- ceedings of the Workshop on Adaptive Text Extrac- tion and Mining (ATEM 2006).",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Detecting hate speech on the world wide web",
"authors": [
{
"first": "William",
"middle": [],
"last": "Warner",
"suffix": ""
},
{
"first": "Julia",
"middle": [],
"last": "Hirschberg",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the Second Workshop on Language in Social Media",
"volume": "",
"issue": "",
"pages": "19--26",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "William Warner and Julia Hirschberg. 2012. Detecting hate speech on the world wide web. In Proceedings of the Second Workshop on Language in Social Me- dia, pages 19-26, Montr\u00e9al, Canada.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Are you a racist or am i seeing things? annotator influence on hate speech detection on twitter",
"authors": [
{
"first": "Zeerak",
"middle": [],
"last": "Waseem",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the first workshop on NLP and computational social science",
"volume": "",
"issue": "",
"pages": "138--142",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zeerak Waseem. 2016. Are you a racist or am i seeing things? annotator influence on hate speech detection on twitter. In Proceedings of the first workshop on NLP and computational social science, pages 138- 142.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Understanding abuse: A typology of abusive language detection subtasks",
"authors": [
{
"first": "Zeerak",
"middle": [],
"last": "Waseem",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "Davidson",
"suffix": ""
},
{
"first": "Dana",
"middle": [],
"last": "Warmsley",
"suffix": ""
},
{
"first": "Ingmar",
"middle": [],
"last": "Weber",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weber. 2017. Understanding abuse: A typology of abusive language detection subtasks. CoRR, abs/1705.09899.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Hateful symbols or hateful people? predictive features for hate speech detection on twitter",
"authors": [
{
"first": "Zeerak",
"middle": [],
"last": "Waseem",
"suffix": ""
},
{
"first": "Dirk",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the NAACL student research workshop",
"volume": "",
"issue": "",
"pages": "88--93",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zeerak Waseem and Dirk Hovy. 2016. Hateful sym- bols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of the NAACL student research workshop, pages 88-93.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Why newsrooms need a solution to end online harassment of reporters",
"authors": [
{
"first": "Lucy",
"middle": [],
"last": "Westcott",
"suffix": ""
},
{
"first": "James",
"middle": [
"W"
],
"last": "Foley",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lucy Westcott and James W Foley. 2019. Why newsrooms need a solution to end online harassment of reporters. https: //cpj.org/2019/09/newsrooms-solution- online-harassment-canada-usa/.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Twitter block chain",
"authors": [
{
"first": "Cecilia",
"middle": [],
"last": "Wren",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Cecilia Wren. 2019. Twitter block chain. https://chrome.google.com/ webstore/detail/twitter-block-chain/ dkkfampndkdnjffkleokegfnibnnjfah?hl= en/.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Ex machina: Personal attacks seen at scale",
"authors": [
{
"first": "Ellery",
"middle": [],
"last": "Wulczyn",
"suffix": ""
},
{
"first": "Nithum",
"middle": [],
"last": "Thain",
"suffix": ""
},
{
"first": "Lucas",
"middle": [],
"last": "Dixon",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 26th International Conference on World Wide Web",
"volume": "",
"issue": "",
"pages": "1391--1399",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ellery Wulczyn, Nithum Thain, and Lucas Dixon. 2017. Ex machina: Personal attacks seen at scale. In Proceedings of the 26th International Conference on World Wide Web, pages 1391-1399.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "Annotation platform user interface.",
"num": null,
"uris": null,
"type_str": "figure"
},
"FIGREF1": {
"text": "Fetch thread from tweet 1: procedure FETCH THREAD(id, api) participants are currently presented with the following labels: hateful, abusive, neutral, or spam.",
"num": null,
"uris": null,
"type_str": "figure"
}
}
}
} |