An MIT research team has published a paper in Science detailing their analysis of the virulence with which truth and falsehood spread on Twitter; they analyzed 126,000 stories tweeted by 3m people 4.5m times, characterizing the stories as true or false according to consensus among a pool of independent fact-checking organizations, and concluded that “falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information.”
Lies, they concluded, were more interesting than the truth, because they were unconstrained by the ambiguity and nuance that makes truth less shareable than lies.
They found bots sharing truth and lies, and concluded that this happened at the same rate regardless of the veracity of the stories. They say that this shows that the virulence of lies is down to humans, not bots.
Although false rumors were measurably more novel than true rumors, users may not have perceived them as such. We therefore assessed users’ perceptions of the information contained in true and false rumors by comparing the emotional content of replies to true and false rumors. We categorized the emotion in the replies by using the leading lexicon curated by the National Research Council Canada (NRC), which provides a comprehensive list of ~140,000 English words and their associations with eight emotions based on Plutchik’s (31) work on basic emotion—anger, fear, anticipation, trust, surprise, sadness, joy, and disgust (32)—and a list of ~32,000 Twitter hashtags and their weighted associations with the same emotions (33). We removed stop words and URLs from the reply tweets and calculated the fraction of words in the tweets that related to each of the eight emotions, creating a vector of emotion weights for each reply that summed to one across the emotions. We found that false rumors inspired replies expressing greater surprise (K-S test = 0.205, P ~ 0.0), corroborating the novelty hypothesis, and greater disgust (K-S test = 0.102, P ~ 0.0), whereas the truth inspired replies that expressed greater sadness (K-S test = 0.037, P ~ 0.0), anticipation (K-S test = 0.038, P ~ 0.0), joy (K-S test = 0.061, P ~ 0.0), and trust (K-S test = 0.060, P ~ 0.0) (Fig. 4, D and F). The emotions expressed in reply to falsehoods may illuminate additional factors, beyond novelty, that inspire people to share false news. Although we cannot claim that novelty causes retweets or that novelty is the only reason why false news is retweeted more often, we do find that false news is more novel and that novel information is more likely to be retweeted.
The spread of true and false news online [Soroush Vosoughi, Deb Roy and Sinan Aral/Science]
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(Image: Mike Licht, CC-BY)
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