Fake news on Twitter. Lisa Friedland, Kenny Joseph, Nir Grinberg, David Lazer Northeastern University
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1 Fake news on Twitter Lisa Friedland, Kenny Joseph, Nir Grinberg, David Lazer Northeastern University
2 Case study of a fake news pipeline Step 1: Wikileaks acquires hacked s from John Podesta Step 2: Wikileaks publishes s
3 Case study (cont.) Step 3a: Fake news derived from leaks Step 3b: Diffusion & misinformation campaigns
4 Case study (cont.) Step 4: Potential Impacts on voters
5 Flow of fake news about Podesta Websites Introduced to Twitter Panel Members
6 Fake news on Twitter 1. Data: Americans sharing URLs on Twitter 2. How prevalent was fake news on Twitter? 3. Who shared fake news? 4. How did it spread through Twitter?
7 Studying humans on Twitter If we want to know how fake news affected the election, look at voters!
8 Linking Twitter Users to Voter Records Twitter universe Name State Party Age Match to Twitter? Kenny Joseph Nir Grinberg Nir Grinberg Amelia Tzray No match, not in voter records Voter registrations MA D 52 Yes! NY D 19 No, not unique NY R 25 No, not unique CA No, not on Twitter Accounts we follow Result: panel of ~22k U.S. voters on Twitter
9 We focus on ~100K political URL tweets that: a) mention Clinton or Trump b) were sent in 2016 c) have URLS (not back to Twitter)
10 Defining fake news Compiled list of 449 fake news sources (websites) Aggregation of existing lists from Hoaxy, BuzzFeed, Media Bias Fact Check, To validate coverage, manually examine popular URLs from fake and uncategorized sources Does it have the veneer of legitimate news? Is the content truthful?
11 Exploring fake and real news Much political content (both real and fake) on the web resembles clickbait or blogs Casual writing style, hyperlinks to story sources, highly partisan We see a spectrum of truthfulness. Often hard to distinguish among: Clickbait real Misleading fake Conspiracy fake Fictional fake (Easiest to recognize, but rare)
12 How well do the lists capture fake news? On fake news lists: fairly sketchy content Not on lists: still 4% of URLs are sketchy The categorizations can be improved, but these are reasonable for a first pass Includes Misleading, Conspiracy, and Fictional categories
13 Fake news on Twitter 1. Data: Americans sharing URLs on Twitter 2. How prevalent was fake news on Twitter? 3. Who shared fake news? 4. How did it spread through Twitter?
14 Fake news was more prevalent than you might expect 5,503 out of 95,660 (5.8%) links in 2016 were to fake news Most prominent in months leading up to election
15 It was even prominent compared to individual real news sources
16 Ranking of popularity of fake news Rank Website % Republicans w/ 1 URL from site % Democrats w/ 1 URL from site 1 NY Times Youtube Washington Post CNN Facebook Politico The Hill Fake News Breitbart 7 1
17 The most popular fake news sites were right leaning Top 50 overall ( ) & fake news ( ) sites by % Dems, Reps who tweeted out at least 1 URL from site
18 Fake news on Twitter 1. Data: Americans sharing URLs on Twitter 2. How prevalent was fake news on Twitter? 3. Who shared fake news? 4. How did it spread through Twitter?
19 Fake news sharing was heavy-tailed 4,790 users (of 22K) shared at least 1 political URL Of those, 7% (348) shared a fake news URL 70% of fake news was shared by a set of 15 people! More evenly distributed than Fox, similar to Breitbart Less evenly distributed than traditional news outlets, social media
20 Who are the super sharers? Real people Highly active users Use automation at times Super-sharers of fake news tend to be super-sharers of news in general
21 Fake news on Twitter 1. Data: Americans sharing URLs on Twitter 2. How prevalent was fake news on Twitter? 3. Who shared fake news? 4. How did fake news spread through Twitter?
22 Fake news tweets and retweets Websites Introduced to Twitter Panel Members Fake news dominated by 3 websites, 15 super-sharers Many introducers Panel members seem to retweet without regard to the introducer
23 Retweets: Fake news vs. all political URLs Websites Introduced to Twitter Panel Members Websites Introduced to Twitter Panel Members
24 Conclusions Fake news was among the most popular news content shared on Twitter in October 2016 A small number of websites were main producers of fake news content (subject to our definitions) Most fake news (70%) was shared by a small number of supersharers Only 15 in a 22K sample (of non-bots) Had cyborg tendencies Other 30% shared by right leaning, older people Channels where fake news was shared: Often cross-posted from Facebook Different, highly partisan, intermediate sources Voters who shared fake news did so without much regard to specific primary or intermediate sources
25 kelsthesuperninjabat.blogspot.com Fake News Detector
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