SlowGuess/ACL_2020_OCR huggingface dataset:
As for left-leaning accounts, they include those attacking Trump and the Republicans, and expressing support for the Democratic party and for Liberal social positions. If the retweeted account happens to be a media source, we used mediaBiasFactCheck.com. Table 5 compares the per-topic valence for each retweeted account along with the average category and the true label.
It is noteworthy that all top-200 retweeted accounts have extreme valence categories on average across all topics. Their average valence scores, with one exception, appear between  $-0.6$  and  $-1.00$  for right, and between 0.6 and 1 for left (see Figure 4).
Of those manually and independently tagged accounts, all that were tagged as left-leaning have a strong positive valence score and all that were tagged as right-leaning have a strong negative valence score. Only two accounts were manually labeled as center, namely Reuters and CSPAN, which is a US channel that broadcasts Federal Government proceedings, and they had valence scores of 0.55 and 0.28, respectively. Though their absolute values are lower than those of all other sources, they are mapped to the + valence category.
Table 3 summarizes the valence scores for the media across all topics. Table 4 lists the most cited media sources for each topic and for each of the five valence bands. The order of the bands from top to bottom is: ++, +, 0, - and --. The table also includes the credibility and the political leaning tags from mediaBiasFactCheck.com. The key observations from the table as follows:
1. Most right-leaning media appear overwhelmingly in the -- and -- valence categories. Conversely, left-leaning media appear in all valence categories, except for the -- category. This implies that left-leaning users cite right-leaning media sparingly. We looked at some instances where right-leaning users cited left-leaning media, and we found that in many cases the cited articles reinforced a right-leaning viewpoint. For example, right-leaning users shared a video from thehill.com, a left-center site, 2,398 times for the police racism topic. The video defended Trump against charges of racism by Lynne Patton, a longtime African-American associate of Trump.
Medium	factuality	bias	Average	climate change	gun control	ilhan	immigration	midterm	police & racism	Kavanaugh	vaccine
thehill.com	H	L-C	+	0	++	+	+	+	+	++	++
theguardian.com	H	L-C	++	++	++	++	++	++	++	++	++
washingtonpost.com	H	L-C	++	++	++	++	++	++	++	++	++
breitbart.com	VL	Far R	--	--	--	--	--	--	--	--	--
foxnews.com	M	R	--	--	--	--	--	--	--	--
nytimes.com	H	L-C	++	+	++	+	+	+	++	++	++
cn.com	M	L	+	+	++	+	++	+	+	++	+
apple.news	+	0	0	+	0	0	+	+	++
daily caller.com	M	R	--	--	--	--	--	--	--	--
rawstory.com	M	L	++	++	++	++	++	++	++	++	++
huffingtonpost.com	H	L	++	++	++	++	++	+	++	++	++
truepundit.com	L	--	--	--	--	--	--	--	--	--
nbcnews.com	H	L-C	+	--	++	+	++	+	+	++	++
westernjournal.com	M	R	--	--	--	--	--	--	--	--
reuters.com	VH	C	+	+	++	++	+	+	+	+	++
washingtonexaminer.com	H	R	--	--	--	--	--	0	--	--
thegatewaypundit.com	VL	Far R	--	--	--	--	--	--	--	--
politico.com	H	L-C	+	+	+	+	+	++	+	+	++
npr.org	VH	L-C	+	0	++	++	++	0	++	++	++
townhall.com	M	R	--	--	--	--	--	--	--	--	--
msn.com	H	L-C	+	+	+	+	0	++	0	++	0
nypost.com	M	R-C	-	--	0	-	-	+	--	-
vox.com	H	L	++	++	++	++	++	++	+	++	++
thedailybeast.com	H	L	++	++	++	+	++	++	+	++	++
bbc.com	H	L-C	+	+	+	++	++	0	+	+	++
independent.co.uk	H	L-C	++	++	+	++	++	++	+	++	++
ilovemyfreedom.org	VL	Far R	--	--	--	--	--	--	--	--
thinkprogress.org	M	L	++	++	++	++	++	++	++	++	++
dailywire.com	M	R	--	--	--	--	--	--	--	--	++
pscp.tv	-	--	--	--	0	--	0	-
dailymail.co.uk	VL	R	-	-	0	-	-	-	-	--	--
msnbc.com	M	L	++	++	++	++	++	+	++	++
dailykos.com	M	L	++	++	++	++	++	+	++	++
bloomberg.com	H	L-C	+	+	++	0	++	+	0	+	++
usatoday.com	H	L-C	+	+	+	0	+	++	+	0	+
Table 3: Media valence categories for each topic with included average column. Plus (+) and minus (-) signify left or right leaning, respectively. Factuality: Very High (VH), High (H), Mixed (M), Low (L), Very Low (VL). Bias: Left (L), Left-Center (L-C), Center (C), Right-Center (R-C), Right (R), Far Right (Far R). Blank cells mean that we did not have information.
2. Most right-leaning sources in the -- category have mixed, low, or very low factuality. Conversely, most left-leaning sites appearing in the -- valence category have high or very high factuality. Similarly for the vaccine topic, where high credibility sources, such as fda.gov and nih.gov, are frequently cited by anti-vaccine users, mostly to support their beliefs.
3. The placements of sources in different categories are relatively stable across topics. For example, washingtonPost.com and theguardian.com exclusively appear in the ++ category, while breitbart.com and foxnews.com consistently appear in the -- category.
6 Predicting Media Bias
Given the stances of users on the aforementioned eight topics, we leverage this information to predict media bias. Specifically, we describe in this section how we make use of the valence scores, as well as other features, namely graph and contextualized text embeddings, to train supervised classifiers for this purpose.
Valence Scores. We use valence scores in two ways. First, we average the corresponding valence across the different polarizing topics to obtain an average valence score for a given target news medium. This is an unsupervised method for computing polarity. Second, we train a Logistic Regression classifier that uses the calculated valence scores as features and annotations from mediaBiasFactCheck.com as gold target labels in order to predict the general political leaning of a target news medium. We merged "left" and "extreme left", and similarly we merged "right" and "extreme right". We discarded media labeled as being "left-center" and "right-center". Each news medium was represented by an 8-dimensional vector containing the valence scores for the above topics. In the experiments, we used the lbfgs solver and  $C = 0.1$ . We used two measures to evaluate its performance, namely accuracy and mean absolute error (MAE). The latter is calculated by considering the different classes as ordered and equally distant from each other, i.e., if the model predicts right and the true label is left, this amounts to an error equal to 2.
climate change	gun control	Ilhan Omar	immigration
theguardian.com	H	L-C	thehill.com	H	L-C	washingtonpost.com	H	L-C	theguardian.com	H	L-C
washingtonpost.com	H	L-C	cnn.com	M	L	theguardian.com	H	L-C	washingtonpost.com	H	L-C
independent.co.uk	H	L-C	nytimes.com	H	L-C	monday.com	H	L	cnn.com	M	L
wef.ch	npr.org	VH	L-C	thinkprogress.org	M	L	huffingtonpost.com	H	L
vox.com	H	L	washingtonpost.com	H	L-C	haaretz.com	H	L-C	npr.org	VH	L-C
nytimes.com	H	L-C	politico.com	H	L-C	nytimes.com	H	L-C	thehill.com	H	L-C
bbc.com	H	L-C	usatoday.com	H	L-C	thehill.com	H	L-C	nytimes.com	H	L-C
cnn.com	M	L	msn.com	H	L-C	politico.com	H	L-C	reuters.com	VH	C
reuters.com	VH	C	bbc.com	H	L-C	cnn.com	M	L	politico.com	H	L-C
bloomberg.com	H	L-C	cnbc.com	H	L-C	apple.news	usatoday.com	H	L-C
thehill.com	H	L-C	apple.news	H	R-C	mediaite.com	H	L	apple.news
apple.news	sun-sentinel.com	H	R-C	usatoday.com	H	L-C	msn.com	H	L-C
npr.org	VH	L-C	nypost.com	M	R-C	yahoo.com	M	L-C	pscp.tv
seattletimes.com	H	L-C	dailymail.co.uk	VL	R	timesofisrael.com	H	L-C	whitehouse.gov	M	R
newsweek.com	M	L	mailchi.mp	theatlantic.com	H	L-C	texastribune.org	H	C
change.org	H	L	washingontimes.com	H	R-C	nypost.com	M	R-C	dailymail.co.uk	VL	R
latimes.com	H	L-C	breaking911.com	VL	jpost.com	H	R	nypost.com	M	R-C
dailymail.co.uk	VL	R	chicagotribune.com	H	R-C	dailymail.co.uk	VL	R	zerohedge.com	M
climatechangeddispatch.com	rt.com	M	R-C	algemeiner.com	H	R-C	ir/shareaholic.com
cnbc.com	H	L-C	forbes.com	M	R-C	startribune.com	H	L-C	breaking911.com	VL
forbes.com	M	R-C	breibart.com	VL	Far R	foxnews.com	M	R	breibart.com	VL	Far R
breibart.com	VL	Far R	foxnews.com	M	R	breibart.com	VL	Far R	illegalaliencrimereport.com
dailycaller.com	M	ammoland.com	H	R	townhall.com	M	R	washingtonexaminer.com	H	R
tambonthongchai.com	dailycalleder.com	M	R	change.org	H	L	foxnews.com	M	R
wattsupwiththat.com	L	bearingarms.com	M	R	hannity.com	westernjournal.com	M	R
midterm	police & racism	Kavanaugh	vaccine
washingtonpost.com	H	L-C	washingtonpost.com	H	L-C	thehill.com	H	L-C	thehill.com	H	L-C
theguardian.com	H	L-C	rawstory.com	M	L	washingtonpost.com	H	L-C	theguardian.com	H	L-C
rawstory.com	M	L	huffingtonpost.com	H	L	cnn.com	M	L	washingtonpost.com	H	L-C
tacticalinvestor.com	theguardian.com	H	L-C	nytimes.com	H	L-C	vaxpedia.org
vox.com	H	L	nytimes.com	H	L-C	huffingtonpost.com	H	L	nytimes.com	H	L-C
thehill.com	H	L-C	thehill.com	H	L-C	politico.com	H	L-C	cnn.com	M	L
reuters.com	VH	C	apple.news	apple.news	statnews.com	H	C
nytimes.com	H	L-C	cnn.com	M	L	yahoo.com	M	L-C	latimes.com	H	L-C
cnn.com	M	L	nbnews.com	H	L-C	apnews.com	VH	C	cbc.ca	H	L-C
dailykos.com	M	L	thedailybeast.com	H	L	latimes.com	H	L-C	usatoday.com	H	L-C
apple.news	msn.com	H	L-C	usatoday.com	H	L-C	cdc.gov	VH
sagagist.com.ng	pscp.tv	mediaite.com	H	L	medium.com	M	L-C
bbc.com	H	L-C	bloomberg.com	H	L-C	theweek.com	H	L-C	newroom.fl.com
alzwaaj.com	politics.theion.com	lawandcrime.com	help.senate.gov
washingtonexaminer.com	H	R	rollcall.com	VH	C	cnbc.com	H	L-C	msn.com	H	L-C
dailymail.co.uk	VL	R	mediaite.com	H	L	pscp.tv	change.org	H	L
pbs.org	H	L-C	dailymail.co.uk	VL	R	nypost.com	M	R-C	fda.gov
zerohedge.com	M	news.sky.com	H	L-C	ir/shareaholic.com	variety.com
ajc.com	H	L-C	newson.com	H	L-C	rollcall.com	VH	C
veritablenouvelordre.forumca.ada. org	aol.com	H	L-C	c-span.org	VH	C
breitbart.com	VL	Far R	breitbart.com	VL	Far R	foxnews.com	M	R	ncbi.nlm.nih.gov	VH
foxnews.com	M	R	defensemaven.io	truepundit.com	L	vaccineimpact.com
dailycaller.com	M	R	foxnews.com	M	R	dailypayer.com	M	R	naturalnews.com	M
ilovemyfreedom.org	VL	Far R	thegatewaypundit.com	VL	Far R	breitbart.com	VL	Far R	vaccines.me
westernjournal.com	M	R	nypost.com	M	R-C	thegateypundit.com	VL	Far R	thevaccinereaction.org
Table 4: Top 5 websites per valence category for each topic.
Account	Truth	Average	climate change	gun control	ilhan	immigration	midterm	police & racism	Kavanaugh	vaccine
realdonaldtrump	R	--	0	--	--	--	--	--	--
charliekirk11	R	--	--	--	--	--	--	--
kylegriffin1	L	++	++	++	++	++	++	++	++
dbmongino	R	--	--	--	--	--	--	--	--
kamalaharris	L	++	++	++	++	++	++	++
mitchellvii	R	--	--	--	--	--	--	--	--
realsaavedra	R	--	--	--	--	--	--	--
krassenstein	L	++	++	++	++	++	++	++	++
realjack	R	--	--	--	--	--	--	--	--	--
nbcnews	L	++	++	++	+	++	++	++	++	++
education4ibs	R	--	--	--	--	--	--	--	--
nra	R	--	--	--	--	--
donaldjtrumpjr	R	--	--	--	--	--	--
shannonrwatts	L	++	++	++	++	++	++
thehill	L	++	++	++	+	++	+	+	++	++
realjameswoods	R	--	--	--	--	--	--	--
gopchairwoman	R	--	--	--	--	--
jackposobiec	R	--	--	--	--	--	--	--	--
funder	L	++	++	++	++	++	++	++	++
cnn	L	++	++	++	++	++	0	++	++	++
ajplus	L	++	++	++	++	++	++	++	0	++
rashidataib	L	++	++	++	++	++	+
stevescalise	R	--	--	--	--
jordan_sather_	?	--	--	--	--	--	--
aoc	L	++	++	++	++	++
Table 5: User valence categories for each topic, preceded by an average column, and a ground truth label. When a cell is blank, there is insufficient data for that particular topic.
No Valence	With Valence
Acc	MAE	Acc	MAE
Baseline 1 (majority class)	43.3	.856	43.3	.856
Baseline 2 (average valence)	-	-	68.0	.330
Valence scores	-	-	75.2	.278
BERT (article title)	60.6	.539	78.3	.264
BERT (article content)	61.1	.526	79.2	.255
BERT (title+content)	62.2	.510	80.8	.228
BERT(Tweet)	64.0	.485	73.6	.302
GraphEmbM	63.5	.468	69.1	.380
GraphEmbH	66.9	.425	71.8	.347
GraphEmbM+H	68.0	.400	79.0	.251
GraphEmbM+H+BERT (tweet)	72.5	.358	80.5	.230
GraphEmbM+H+BERT (tweet, content)	76.1	.311	81.2	.221
GraphM+H+BERT (tweet, title, content)	78.1	.284	82.6	.206
Table 6: Predicting media bias.
The results are shown in Table 6, where we can see that using the average valence score yields  $68.0\%$  accuracy (0.330 MAE) compared to  $75.2\%$  accuracy (0.278 MAE) when using the eight individual valence scores as features.
Graph embeddings. We further use graph embeddings, generated by building a User-to-Hashtag graph (U2H) and a User-to-Mention (U2M) graph and then running node2vec on both (Atanasov et al., 2019), producing two types of graph embeddings. When using graph embeddings, we got worse results compared to our previous setup with valence scores (see Table 6). However, when we combine them with the valence scores, we observe a sizable boost in performance, up to  $11\%$  absolute.
Tweets. We also experimented with BERT-base. We used the text of the tweets that cite the media we are classifying. For classification, we fed BERT representations of tweets to a dense layer with softmax output to fine-tune it with the textual contents of the tweets. We trained at the tweet level, and we averaged the scores (from softmax) for all tweets from the same news medium to obtain an overall label for that news medium. The accuracy is much lower than for the valence scores:  $64.0\%$  accuracy vs.  $75.2\%$  for supervised and  $68.0\%$  for unsupervised.
Article titles and text. Using the BERT setup for Tweets, we used the titles and the full text of up to 100 articles from each of the target media. When using the full text of articles, we balanced the number of articles per news medium. We trained two separate BERT models, one on the titles and another one on the full text (content). Both models did worse than using valence alone, but the combination improved over valence only.
System Combination. We combined different setups including using all the aforementioned models in combination. Using graph embeddings (GraphH + GraphM) with BERT embeddings (Tweet+Title+Content) and valence yielded the best results with accuracy of  $82.6\%$  and MAE of .206. If we remove valence from the combination, the accuracy drops by  $4.5\%$  while MAE jumps by .078, absolute. This suggests that valence is a very effective feature that captures important information, complementary to what can be modeled using graph and contextualized text embeddings.
We have presented a method for predicting the general political leaning of media sources and popular Twitter users, as well as their stances on specific polarizing topics. Our method uses retweeted accounts, and a combination of dimensionality reduction and clustering algorithms, namely UMAP and Mean Shift, in order to produce sets of users that have opposing opinions on specific topics. Next, we expand the discovered sets using supervised learning that is trained on the automatically discovered user clusters. We are able to automatically tag large sets of users according to their stance of preset topics. Users' stances are then projected to the influencers that are being cited in the tweets for each of the topics using the so-called valence score. The projection allows us to tag a large number of influencers with their stances on specific issues and with their political leaning in general (i.e., left vs. right) with high accuracy and with minimal human effort. The main advantage of our method is that it does not require manual labeling of entity stances, which requires both topical expertise and time. We also investigated the quality of the valence features, and we found that valence scores help to predict media bias with high accuracy.
In future work, we plan to increase the number of topics that we use to characterize media. Ideally, we would like to automatically identify such polarizing topics. Doing so would enable us to easily retarget this work to new countries and languages.
This research is part of the Tanbih project<sup>1</sup>, which aims to limit the effect of "fake news," propaganda and media bias by making users aware of what they are reading.
http://tanhbih.qcri.org/
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