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Antisocial Computing

Justin Cheng
February 02, 2018

Antisocial Computing

Justin Cheng

February 02, 2018
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  1. Justin Cheng / @jcccf / clr3.com with Jure Leskovec, Michael

    Bernstein, Cristian Danescu-Niculescu-Mizil, and Tim Hsieh ANTISOCIAL COMPUTING Explaining, Predicting, and Mediating Online Negative Behavior v1.1
  2. Antisocial behavior is largely due to sociopaths Prior Work Donath

    (1999); Hardaker (2010); Buckels, et al. (2014)
  3. Antisocial Computing Talk Outline What causes antisocial behavior? How do

    systems mediate it? Does it worsen over time? 1 2 3 WIP!
  4. Antisocial Computing Talk Outline What causes antisocial behavior? How do

    systems mediate it? Does it worsen over time? 1 2 3 WIP!
  5. CSCW 2017 (Best Paper); ICWSM 2015 (Honorable Mention) with M.

    Bernstein, C. Danescu-Niculescu-Mizil, J. Leskovec CAN ANYONE BECOME A TROLL? Causes of Antisocial Behavior in Online Discussions
  6. It also shows that Islam and Christianity teaching women to

    dress modest could be right afterall.
  7. It also shows that Islam and Christianity teaching women to

    dress modest could be right afterall. Clearly that is the only logical conclusion to this article. Now if you'll excuse me, I need to iron my tarp. I have work on Monday, and I want to appear 'modest'. fail at life. go bomb yourself. Religious nut alert
  8. What is trolling? Engaging in negatively marked online behavior Taking

    pleasure in upsetting others Not following the rules Disrupting a group while staying undercover Donath (1999); Hardaker (2010); Kirman (2012); Schwartz (2008)
  9. Trolling is behavior that occurs outside community norms. Defined using

    community guidelines Our Definition e.g., name-calling, personal attacks, profanity, threats, hate speech, ethnically/racially offensive material
  10. Are trolls just a vocal minority? Donath (1999); Hardaker (2010);

    Shachaf & Hara (2010); NYT (2008); Wired (2014); Vox (2014)
  11. How much do trolls troll? Proportion of Banned Users 0

    0.1 0.2 0.3 0.4 Proportion of Deleted Posts 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  12. The distribution of trolls is bimodal Proportion of Banned Users

    0 0.1 0.2 0.3 0.4 Proportion of Deleted Posts 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
  13. Are there two types of trolls? Proportion of Banned Users

    0 0.1 0.2 0.3 0.4 Proportion of Deleted Posts 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Situational trolling? Lifelong trolling?
  14. How did trolling differ across conditions? Positive Mood Negative Mood

    Positive Context Negative Context % Troll Posts
  15. Trolling is lowest with positive conditions… Positive Mood Negative Mood

    Positive Context 35% Negative Context % Troll Posts
  16. …and almost doubles in the worst case Positive Mood Negative

    Mood Positive Context 35% 49% Negative Context 47% 68% % Troll Posts (p < 0.05 using a mixed effects logistic regression model)
  17. Negative affect almost triples Positive Mood Negative Mood Positive Context

    1.1% 1.4% Negative Context 2.3% 2.9% % Negative Affect Words (LIWC) (p < 0.05)
  18. Hilary is a solid candidate. As a woman, I appreciate

    that she's a woman, but it's not the only reason I think she would do well in office. Positive Mood + Context
  19. Anyone who votes for her is a complete idiot. These

    supporters are why this country is in such bad shape now. Uneducated people. Negative Mood + Context
  20. Can trolling, like mood, vary with the time of day

    and day of week? Replicating Mood Golder & Macy (2011) Neg. Affect Time of Day ?
  21. How does trolling vary with time of day? Proportion of

    Flagged Posts 0.03 0.033 0.036 0.039 0.042 Time of Day 0 6 12 18 24
  22. Trolling peaks in the evening… Proportion of Flagged Posts 0.03

    0.033 0.036 0.039 0.042 Time of Day 0 6 12 18 24 Negative Affect (Golder & Macy)
  23. …and early in the work week. Proportion of Flagged Posts

    0.03 0.032 0.034 0.036 0.038 0.04 Day of Week Mon Tue Wed Thu Fri Sat Sun
  24. Trolling peaks when moods are worse Time of Day Proportion

    of Flagged Posts Negative Affect Proportion of Downvotes Day of Week
  25. … … … ? … (Discussion) (Unrelated Discussions) … ?

    … Mood spills over from prior discussions
  26. A user who trolled in a previous discussion is twice

    as likely to troll in a later, unrelated discussion (p < 0.01) Replicating Mood
  27. (Separate discussions of same article) … ? ? ? …

    ? ? ? The initial post affects subsequent trolling
  28. Antisocial Computing Talk Outline What causes antisocial behavior? How do

    systems mediate it? Does it worsen over time? 1 2 3 WIP!
  29. Defining positive and negative evaluations : 9 1 2 8

    N↑
 N↑ + N↓ 2
 2+8 = = 0.2 p↓ ≤ : N↑
 N↑ + N↓ 9
 9+1 = = 0.9 p↑ ≥ Positive Evaluation Negative Evaluation (validated using a crowdsourcing experiment)
  30. Solution: propensity score matching PSM: Rosenbaum (1983); CEM: Iacus, et

    al. (2012) Positively evaluated Negatively evaluated
  31. Computing text quality (Validated using crowdsourcing) Learn p with bigrams

    1 3 lorem ipsum… q = ? Lorem… ? ? 9 2 lorem ipsum… … Text quality q is predicted p
  32. …as well as other covariates Similar history (# posts, overall

    proportion of upvotes, etc.) { ≈ … … ≈
  33. How much are evaluations due to textual effects (i.e., people

    writing worse)? f***ing a****** i.e., downvoting because of post content
  34. How much are evaluations due to community effects (i.e., inherent

    bias)? We dislike you. i.e., downvoting because of community dislikes author We dislike you.
  35. ≈ … … … … Better/Worse? Do people write better/worse

    after a positive/negative evaluation? ≈
  36. Text quality drops significantly after a negative evaluation… (p <

    0.05, mean effect size r = 0.18) … … Negativity bias
  37. Measuring community bias N↑ q N↓ N↑
 N↑ + N↓

    = 0.5 p(c) = = 0.8 q(c) p(c) q(c) = 0.3 Prop. Upvotes Text Quality Community Bias − −
  38. Community bias increase more after a negative than positive evaluation

    (p < 0.01, mean effect size r = 0.13) … … Halo effect
  39. Antisocial Computing Talk Outline What causes antisocial behavior? How do

    systems mediate it? Does it worsen over time? 1 2 3 WIP!
  40. Work-in-Progress with J. Leskovec, T. Hsieh WIP! How Introducing Downvoting

    to Communities Impacts User Behavior CAN ENABLING NEGATIVE FEEDBACK HARM COMMUNITIES?
  41. Why the sudden increase in downvotes? Data: Worldstarhiphop circa. 2012

    # Votes 0 40000 80000 120000 160000 Time (Days) 0 20 40 60 80 100 120 140 160 180 200 220 Downvotes Upvotes ? WIP!
  42. How do we measure impact? WIP! Time (Weeks) 1 5

    -4 0 Before After Repeat for each domain (x20), applying corrections as necessary
  43. Does introducing downvoting alter upvoting behavior? ? ? Downvotes don’t

    impact upvotes overall Upvotes increase to offset downvotes WIP! (Muchnik, et al. 2013)
  44. Introducing downvoting has little effect on upvoting overall. (5 domains

    saw a significant increase / 12 saw no change / 3 saw a significant decrease, overall n.s.) WIP!
  45. Introducing downvoting spurs discussion. More replies, less top-level comments Discussion

    length increases … (13 + / 4 · / 3 —, overall increase [p < 0.01]) (15 + / 3 · / 2 —, overall increase [p < 0.01]) WIP!
  46. Introducing downvoting spurs discussion. More replies, less top-level comments Discussion

    length increases More back-and-forth discussion … @john… @mary… @john… @mary… (13 + / 4 · / 3 —, overall increase [p < 0.01]) (15 + / 3 · / 2 —, overall increase [p < 0.01]) (16 + / 2 · / 2 —, overall increase [p < 0.01]) WIP!
  47. Introducing downvoting increases anonymity. WIP! (18 sig. increase / 1

    no change / 1 sig. decrease, overall increase [p < 0.01])
  48. What we now know What we thought Trolls are a

    vocal minority Trolls can be ordinary people Trolling is innate Trolling can spiral from a single bad post Design mediates trolling behavior Design mediates a user’s experience ANTISOCIAL COMPUTING
  49. Justin Cheng / @jcccf / clr3.com with Jure Leskovec, Michael

    Bernstein, Cristian Danescu-Niculescu-Mizil, and Tim Hsieh ANTISOCIAL COMPUTING Explaining, Predicting, and Mediating Online Negative Behavior v1.1