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Anyone Can Become a Troll

Justin Cheng
February 28, 2017

Anyone Can Become a Troll

In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.

Presented at CSCW 2017.

Justin Cheng

February 28, 2017
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  1. (even jcccf, msbernst, cristian, and jure)
    Anyone can become a troll.
    1

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  2. Time (2016)
    “How trolls are ruining the internet”
    2
    News headlines

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  3. The Atlantic (2016)
    “When will the internet be safe for women?”
    “How trolls are ruining the internet”
    3
    News headlines

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  4. Salon (2014)
    “Furious trolls are everywhere”
    “When will the internet be safe for women?”
    “How trolls are ruining the internet”
    4
    News headlines

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  5. Pew Research (2014)
    40% of online users have been harassed
    5

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  6. Popular Science (2013)
    “Why we’re shutting off our comments”
    6
    More headlines

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  7. The Verge (2013)
    “We’re turning comments off for a while”
    “Why we’re shutting off our comments”
    7
    More headlines

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  8. Chicago Sun-Times (2014)
    “Sick of internet comments? Us, too”
    “We’re turning comments off for a while”
    “Why we’re shutting off our comments”
    8
    More headlines

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  9. Why is trolling so prevalent?
    RQ
    9

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  10. Understanding trolling lets us design ❤ communities
    Implication
    10
    healthier prosocial

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  11. What is trolling?
    11

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  12. Donath (1999); Hardaker (2010); Kirman (2012); Schwartz (2008)
    1. Engaging in negatively marked online behavior?
    What is trolling?
    12

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  13. Donath (1999); Hardaker (2010); Kirman (2012); Schwartz (2008)
    2. Not following the rules?
    1. Engaging in negatively marked online behavior?
    What is trolling?
    13

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  14. Donath (1999); Hardaker (2010); Kirman (2012); Schwartz (2008)
    3. Taking pleasure in upsetting others?
    2. Not following the rules?
    1. Engaging in negatively marked online behavior?
    What is trolling?
    14

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  15. Trolling is behavior outside community norms.
    3. Taking pleasure in upsetting others?
    2. Not following the rules?
    1. Engaging in negatively marked online behavior?
    What is trolling?
    15
    name-calling personal attacks threats hate speech ethnically/racially offensive material

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  16. Donath (1999); Hardaker (2010); Buckels, et al. (2014)
    Trolling is largely due to sociopaths
    Prior work
    16

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  17. Trolling is due to ordinary people
    This work
    17

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  18. How much do trolls troll?
    18

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  19. 16M posts on 16K articles from .com
    19
    Data

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  20. 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
    20

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  21. 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
    21

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  22. 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?
    22
    Lifelong trolling?

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  23. How to show that trolling is situational?
    23
    Challenge

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  24. Observational data isn’t causal
    How to show that trolling is situational?
    24

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  25. Experiments are hard to generalize
    Observational data isn’t causal
    How to show that trolling is situational?
    25

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  26. Solution: online experiment + observational study
    Experiments are hard to generalize
    Observational data isn’t causal
    How to show that trolling is situational?
    26

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  27. Anyone can become a troll
    27
    Hypothesis

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  28. Zimbardo (1969); Wilson (1982)
    Theory 1: “Broken windows”
    28

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  29. Jones & Bogat (1978); Rotton & Frey (1985)
    Theory 2: Unpleasant stimuli increase aggression
    29

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  30. Online experiment simulating a discussion forum
    30
    N=667 (40% female)

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  31. Complete a quiz, then participate in a discussion
    Online experiment simulating a discussion forum
    31

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  32. We manipulated quiz difficulty and discussion context
    Complete a quiz, then participate in a discussion
    Online experiment simulating a discussion forum
    32

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  33. The quiz was either easy or difficult
    We manipulated quiz difficulty and discussion context
    Complete a quiz, then participate in a discussion
    Online experiment simulating a discussion forum
    33

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  34. Discussion context was either positive or negative
    The quiz was either easy or difficult
    We manipulated quiz difficulty and discussion context
    Complete a quiz, then participate in a discussion
    Online experiment simulating a discussion forum
    34

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  35. Here’s the easy quiz (positive mood condition)
    35

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  36. Here’s the easy quiz (positive mood condition)
    36

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  37. Here’s the difficult quiz (negative mood condition)
    37

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  38. Here’s the positive discussion context condition
    38

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  39. Here’s negative discussion context condition
    39

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  40. People were in a worse mood after the difficult quiz
    Easy Quiz
    Difficult Quiz
    POMS Mood Disturbance
    0 10 20 30 40 50
    40
    Manipulation checks

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  41. People also perceived seed troll posts as worse
    People were in a worse mood after the difficult quiz
    Seed Troll
    Seed Non-Troll
    Proportion of Upvotes
    0 0.2 0.4 0.6 0.8 1
    41
    Manipulation checks

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  42. How much trolling was there in each condition?
    42
    posts independently labeled by two expert
    raters using community guidelines

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  43. How much trolling was there in each condition?
    Positive
    Mood
    Negative
    Mood
    Positive
    Context
    Negative
    Context
    % Troll Posts
    43

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  44. Trolling is lowest in the positive conditions…
    Positive
    Mood
    Negative
    Mood
    Positive
    Context
    35%
    Negative
    Context
    % Troll Posts
    44

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  45. …increases with either negative condition…
    Positive
    Mood
    Negative
    Mood
    Positive
    Context
    35% 49%
    Negative
    Context
    47%
    % Troll Posts
    45

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  46. …and almost doubles in the negative conditions
    (p < 0.05 using a mixed effects logistic regression model)
    Positive
    Mood
    Negative
    Mood
    Positive
    Context
    35% 49%
    Negative
    Context
    47% 68%
    % Troll Posts
    46

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  47. Negative affect also triples
    (p < 0.05)
    Positive
    Mood
    Negative
    Mood
    Positive
    Context
    1.1% 1.4%
    Negative
    Context
    2.3% 2.9%
    % Neg. Affect Words (LIWC)
    47

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  48. Comment from the positive mood/context condition:
    48

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  49. “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.”
    Comment from the positive mood/context condition:
    49

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  50. Comment from the negative mood/context condition:
    50

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  51. “Anyone who votes for her is a complete idiot. These
    supporters are why this country is in such bad shape
    now. Uneducated people.”
    Comment from the negative mood/context condition:
    51

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  52. Bad mood and negative context increase trolling
    52

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  53. But does this generalize?
    53
    Bad mood and negative context increase trolling

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  54. Online experiment + Observational study
    54
    But does this generalize?
    Bad mood and negative context increase trolling
    .com

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  55. Golder & Macy (2011)
    Can trolling vary with the time of day or day of week?
    55
    Replicating mood
    Neg. Affect
    Time of Day
    ?

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  56. Can trolling vary with the time of day or day of week?
    56
    Proportion of Flagged Posts
    0.03
    0.033
    0.036
    0.039
    0.042
    Time of Day
    0 6 12 18 24

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  57. Trolling peaks in the evening…
    57
    Proportion of Flagged Posts
    0.03
    0.033
    0.036
    0.039
    0.042
    Time of Day
    0 6 12 18 24
    Golder & Macy
    Our Data

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  58. …and early in the work week
    58
    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

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  59. Trolling peaks when moods are worse
    59
    Time of Day
    Proportion of
    Flagged Posts
    Negative Affect
    Proportion of
    Downvotes
    Day of Week

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  60. Does mood spill over from prior discussions?
    60
    Replicating mood

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  61. Does mood spill over from prior discussions?
    61
    (Discussion)


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  62. Does mood spill over from prior discussions?
    62
    (Discussion)




    (Unrelated Discussions)


    ?
    ?

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  63. Mood spills over from prior discussions
    63


    (Discussion)


    (Unrelated Discussions)


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  64. Trolling is twice as likely in unrelated discussions
    (p < 0.01)
    64


    (Discussion)


    (Unrelated Discussions)


    2x

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  65. Does the initial post affect subsequent trolling?
    65
    Replicating context

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  66. Does the initial post affect subsequent trolling?
    66
    (Separate discussions of same article)


    ? ? ?
    ? ? ?

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  67. An initial post increases later trolling by over 1.5x
    (p < 0.01)
    67
    (Separate discussions of same article)


    ? ?

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  68. Does increased trolling have an additive effect?
    68
    Replicating context

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  69. Does increased trolling have an additive effect?
    69
    1 2 3 4 5

    (Discussion)

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  70. Does increased trolling have an additive effect?
    70
    1 2 3 4 5

    (Discussion)

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  71. Does increased trolling have an additive effect?
    71
    1 2 3 4 5

    (Discussion)
    ?

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  72. Does increased trolling have an additive effect?
    72
    1 2 3 4 5

    (Discussion)
    ?

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  73. More initial trolling means more future trolling
    73
    Pr(5th Post is Flagged)
    0
    0.1
    0.2
    0.3
    0.4
    0.5
    0.6
    0.7
    # of Flagged Posts in First 4 Posts
    0 1 2 3 4
    New participant
    Posted previously

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  74. Can we predict trolling before it happens?
    74

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  75. Logistic regression on 120K posts
    75
    Can we predict trolling before it happens?

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  76. Three sets of features: mood, context, or user-specific
    76
    Trolling is situational Trolling is innate
    Logistic regression on 120K posts
    Can we predict trolling before it happens?

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  77. How predictable is trolling?
    77

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  78. How predictable is trolling?
    78
    User-specific
    Mood
    Discussion Context
    Combined
    AUC
    0.00 0.20 0.40 0.60 0.80
    0.78
    0.74
    0.60
    0.66

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  79. How predictable is trolling?
    79
    User-specific
    Mood
    Discussion Context
    Combined
    AUC
    0.00 0.20 0.40 0.60 0.80
    0.78
    0.74
    0.60
    0.66

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  80. How predictable is trolling?
    80
    User-specific
    Mood
    Discussion Context
    Combined
    AUC
    0.00 0.20 0.40 0.60 0.80
    0.78
    0.74
    0.60
    0.66

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  81. 81
    Troll or not?
    User

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  82. 82
    Troll or not?
    User
    Mood

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  83. 83
    Troll or not?
    User
    Mood
    Other
    users
    { }

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  84. Couldn’t we just ban trolls?
    84

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  85. But many “trolls” are ordinary people!
    85
    Couldn’t we just ban trolls?

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  86. Important to also curb situational trolling:
    86
    But many “trolls” are ordinary people!
    Couldn’t we just ban trolls?

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  87. Through inferring mood…
    87
    Important to also curb situational trolling:
    But many “trolls” are ordinary people!
    Couldn’t we just ban trolls?
    Rate limiting Enabling “undo”

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  88. Mazar, Amir, & Ariely (2008)
    Or altering the context of a discussion.
    88
    Through inferring mood…
    Important to also curb situational trolling:
    But many “trolls” are ordinary people!
    Couldn’t we just ban trolls?
    Prioritizing constructive comments Ethical/moral reminders

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  89. (even @jcccf, @msbernst, cristian, and @jure. read the paper: bit.ly/anyonepaper)
    Because trolling is situational,
    anyone can become a troll.
    89

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