Challenges in Modifying Existing Scales for Detecting Harassment in Individual Tweets

In an effort to create new sociotechnical tools to combat online harassment, we developed a scale to detect and measure verbal violence within individual tweets. Unfortunately, we found that the scale, based on scales effective at detecting harassment offline, was unreliable for tweets. Here, we begin with information about the development and validation of our scale, then discuss the scale’s shortcomings for detecting harassment in tweets, and explore what we can learn from this scale’s failures. We explore how rarity, context, and individual coder’s differences create challenges for detecting verbal violence in individual tweets. We also examine differences in on- and offline harassment that limit the utility of existing harassment measures for online contexts. We close with a discussion of potential avenues for future work in automated harassment detection

Descriptors and measurements of verbal violence in tweets: toxicity_first_steps

Sponsorship: National Science Foundation Award # 1525662The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test. In the practice exercise, potential coders attempted to code a tweet on their own using our scale. After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings. The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves. The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks. Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided. APA (6th Edition) CITATION: Guberman, J. and Hemphill, L. (2016) Descriptors and measurements of verbal violence in tweets [data file and codebook]. doi: 10.6084/m9.figshare.3179368

Descriptors and measurements of verbal violence in tweets: toxicity README

Sponsorship: National Science Foundation Award # 1525662The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test. In the practice exercise, potential coders attempted to code a tweet on their own using our scale. After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings. The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves. The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks. Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided. APA (6th Edition) CITATION: Guberman, J. and Hemphill, L. (2016) Descriptors and measurements of verbal violence in tweets [data file and codebook]. doi: 10.6084/m9.figshare.3179368

Descriptors and measurements of verbal violence in tweets

The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test.  In the practice exercise, potential coders attempted to code a tweet on their own using our scale.  After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings.  The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves.  The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks.  Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided

Forecasting the Presence and Intensity of Hostility on Instagram Using Linguistic and Social Features

Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: (1) given an initial sequence of non-hostile comments in a discussion, predict whether some future comment will contain hostility; and (2) given the first hostile comment in a discussion, predict whether this will lead to an escalation of hostility in subsequent comments. Thus, we aim to forecast both the presence and intensity of hostile comments based on linguistic and social features from earlier comments. To evaluate our approach, we introduce a corpus of over 30K annotated Instagram comments from over 1,100 posts. Our approach is able to predict the appearance of a hostile comment on an Instagram post ten or more hours in the future with an AUC of .82 (task 1), and can furthermore distinguish between high and low levels of future hostility with an AUC of .91 (task 2)

Methods, Results, and Discussion

This component contains a summary of our replication's methods, results, and discussion, as well as a poster used by one of the two groups of students involved in this replication

Dataset

Final dataset and the SPSS syntax used to run our analyses. Other file formats can be made available on request