The Impact of Toxic Language on the Health of Reddit Communities

Mohan, Shruthi; Guha, Anirban; Harris, Michael; Popowich, Fred; Schuster, Ashley; Priebe, Chris

doi:10.1007/978-3-319-57351-9_6

Cited by 52 publications

(33 citation statements)

References 4 publications

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“…• The Reddit 13 dataset is composed of over 880,000 comments taken from a wide range of subreddits and annotated a few years ago by the Community Sift tool developed by Two Hat Security 14 . This toxicity detection tool, which was used in previous research on toxicity as well (Mohan et al, 2017), uses over 1 million n-gram rules in order to normalize then categorize each message into one of eight risk levels for a wide array of different categories, 0 to 3 being super-safe to questionable, 4 being unknown and 5 to 7 being mild to severe. In our case, we consider the scores assigned to each message in five categories, namely bullying, fighting, sexting, vulgarity, and racism.…”

Section: Toxicity Detectionmentioning

confidence: 99%

Subversive Toxicity Detection using Sentiment Information

Brassard-Gourdeau¹,

Khoury²

2019

Proceedings of the Third Workshop on Abusive Language Online

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The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection.

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Section: Toxicity Detectionmentioning

confidence: 99%

Subversive Toxicity Detection using Sentiment Information

Brassard-Gourdeau¹,

Khoury²

2019

Proceedings of the Third Workshop on Abusive Language Online

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“…For this research, we chose the popular GloVe vectors from the SpaCy, a free, open-source library for NLP in Python. 17 Other research papers deploying GloVe for online hate detection include, for example, Mishra et al [15] and Kshirsagar et al [90] The GloVe model we apply is publicly available 18 and contains 685k keys and 20k unique vectors with 300 dimensions, trained on Common Crawl datasets. 19…”

Section: Word Embeddingsmentioning

confidence: 99%

“…Even though hate has been observed as a problem in multiple online social media platforms, including Reddit, YouTube, Wikipedia, Twitter, and so on [5,7,[16][17][18], apart from a few exploratory studies [15,19], there is a lack of development and testing of models using data from multiple social media platforms. Instead, studies tend to focus on one platform.…”

mentioning

confidence: 99%

Developing an online hate classifier for multiple social media platforms

Salminen

Hopf²,

Chowdhury

et al. 2020

Hum. Cent. Comput. Inf. Sci.

238

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been identified as a major threat on online social media platforms. Pew Research Center [13] reports that among 4248 adults in the United States, 41% have personally experienced harassing behavior online, whereas 66% witnessed harassment directed towards others. Around 22% of adults have experienced offensive name-calling, purposeful embarrassment (22%), physical threats (10%), and sexual harassment (6%), among other types of harassment. Social media platforms are the most prominent grounds for such toxic behavior. Even though they often provide ways of flagging offensive and hateful content, only 17% of all adults have flagged harassing conversation, whereas only 12% of adults have reported someone for such acts [13].

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“…Toxic commenting has also been found prevalent in general online discussion forums, news websites, and social media platforms. The existing research deals with multiple aspects, such as detection and classification of toxicity [9][10][11], assessing its impact on online communities [12,13], types of toxicity such as cyberbullying and trolling [2,14], and means of defusing online toxicity [15]. To approach toxicity, researchers have investigated multiple social media platforms, such as Twitter, YouTube, Facebook, and Reddit [7,11], as well as comments in online discussion forums and news websites [16].…”

Section: Introductionmentioning

confidence: 99%

“…In the present time, news channels cannot isolate themselves from the audience reactions, but analyzing these reactions is important to understand the various sources of digital bias and to form an analytical relationship to the audience. Finally, the betterment of online experiences by mitigating online toxicity is a matter of societal impact, as toxic conversations impact nearly all online users across social media platforms [10,12,25].…”

Section: Introductionmentioning

confidence: 99%

Topic-driven toxicity: Exploring the relationship between online toxicity and news topics

Salminen

Şengün

Corporan³

et al. 2020

PLoS ONE

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Hateful commenting, also known as 'toxicity', frequently takes place within news stories in social media. Yet, the relationship between toxicity and news topics is poorly understood. To analyze how news topics relate to the toxicity of user comments, we classify topics of 63,886 online news videos of a large news channel using a neural network and topical tags used by journalists to label content. We score 320,246 user comments from those videos for toxicity and compare how the average toxicity of comments varies by topic. Findings show that topics like Racism, Israel-Palestine, and War & Conflict have more toxicity in the comments, and topics such as Science & Technology, Environment & Weather, and Arts & Culture have less toxic commenting. Qualitative analysis reveals five themes: Graphic videos, Humanistic stories, History and historical facts, Media as a manipulator, and Religion. We also observe cases where a typically more toxic topic becomes non-toxic and where a typically less toxic topic becomes "toxicified" when it involves sensitive elements, such as politics and religion. Findings suggest that news comment toxicity can be characterized as topic-driven toxicity that targets topics rather than as vindictive toxicity that targets users or groups. Practical implications suggest that humanistic framing of the news story (i.e., reporting stories through real everyday people) can reduce toxicity in the comments of an otherwise toxic topic.

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The Impact of Toxic Language on the Health of Reddit Communities

Cited by 52 publications

References 4 publications

Subversive Toxicity Detection using Sentiment Information

Subversive Toxicity Detection using Sentiment Information

Developing an online hate classifier for multiple social media platforms

Topic-driven toxicity: Exploring the relationship between online toxicity and news topics

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