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. 2017 Jul 24;12(7):e0181821.
doi: 10.1371/journal.pone.0181821. eCollection 2017.

Debunking in a world of tribes

Fabiana Zollo  1   2 , Alessandro Bessi  3 , Michela Del Vicario  2 , Antonio Scala  2   4 , Guido Caldarelli  2 , Louis Shekhtman  5 , Shlomo Havlin  5 , Walter Quattrociocchi  2
Affiliations

Debunking in a world of tribes

Fabiana Zollo et al. PLoS One. .

Abstract

Social media aggregate people around common interests eliciting collective framing of narratives and worldviews. However, in such a disintermediated environment misinformation is pervasive and attempts to debunk are often undertaken to contrast this trend. In this work, we examine the effectiveness of debunking on Facebook through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users usually consuming proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook US interact with specific debunking posts. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. However, both groups interact similarly with the information within their echo chamber. Then, we measure how users from both echo chambers interacted with 50,220 debunking posts accounting for both users consumption patterns and the sentiment expressed in their comments. Sentiment analysis reveals a dominant negativity in the comments to debunking posts. Furthermore, such posts remain mainly confined to the scientific echo chamber. Only few conspiracy users engage with corrections and their liking and commenting rates on conspiracy posts increases after the interaction.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Users polarization.
Probability density functions (PDFs) of the polarization of all users computed both on likes (left) and comments (right).
Fig 2
Fig 2. Posts’ attention patterns and persistence.
Left panel: Complementary cumulative distribution functions (CCDFs) of the number of likes, comments, and shares received by posts belonging to conspiracy (top) and scientific (bottom) news. Right panel: Kaplan-Meier estimates of survival functions of posts belonging to conspiracy and scientific news. Error bars are on the order of the size of the symbols.
Fig 3
Fig 3. Users’ attention patterns and persistence.
Left panel: Complementary cumulative distribution functions (CCDFs) of the number of comments (top), and likes (bottom), per each user on the two categories. Right panel: Kaplan-Meier estimates of survival functions for users on conspiracy and scientific news. Error bars are on the order of the size of the symbols.
Fig 4
Fig 4. Users’ activity on debunking posts.
Proportions of likes (left) and comments (right) left by users polarized towards science, users polarized towards conspiracy, and not polarized users.
Fig 5
Fig 5. Users’ sentiment on debunking posts.
Sentiment of comments made by all users (left), users polarized towards science (center), and users polarized towards conspiracy (right) on debunking posts having at least a like, a comment, and a share.
Fig 6
Fig 6. Interaction with debunking: Survival functions and attention patterns.
Top panel: Kaplan-Meier estimates of survival functions of users who interacted (exposed) and did not (not exposed) with debunking. Users persistence is computed both on their likes (left) and comments (right). Bottom panel: Complementary cumulative distribution functions (CCDFs) of the number of likes (left) and comments (right), per each user exposed and not exposed to debunking.
Fig 7
Fig 7. Interaction with debunking: Comments and likes rate.
Rate—i.e., average number, over time, of likes (left) (resp., comments (right)) on conspiracy posts of users who interacted with debunking posts.
Fig 8
Fig 8. Cox-Hazard model.
Kaplan-Meier estimates of survival functions of users who interacted (exposed, orange) and did not (not exposed, green) with debunking and fits of the Cox proportional hazard model. Persistence of users is computed both on likes (left) and comments (right).
See this image and copyright information in PMC

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