Analyzing Genetic Testing Discourse on the Web Through the Lens of Twitter, Reddit, and 4chan

Mittos, Alexandros; Zannettou, Savvas; Blackburn, Jeremy; Cristofaro, Emiliano De

doi:10.1145/3404994

“…The classifier provides the probability of a comment being toxic, defined as "a rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion" (Wulczyn, Thain, and Dixon 2017). The Perspective classifier has been used in prior Reddit studies (Mittos et al 2020;Xia et al 2020). Research evaluating its performance on comments from political communities shows that, on average, its toxicity classification is comparable to a single human judgment of toxicity (Rajadesingan, Resnick, and Budak 2020).…”

Section: Quantifying Toxicity Of Repliesmentioning

confidence: 99%

Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)

Rajadesingan

¹

,

Budak

²

,

Resnick

³

2021

ICWSM

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Research on online political communication has primarily focused on content in explicitly political spaces. In this work, we set out to determine the amount of political talk missed using this approach. Focusing on Reddit, we estimate that nearly half of all political talk takes place in subreddits that host political content less than 25% of the time. In other words, cumulatively, political talk in non-political spaces is abundant. We further examine the nature of political talk and show that political conversations are less toxic in non-political subreddits. Indeed, the average toxicity of political comments replying to a out-partisan in non-political subreddits is less than even the toxicity of co-partisan replies in explicitly political subreddits.

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“…The classifier provides the probability of a comment being toxic, defined as "a rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion" (Wulczyn, Thain, and Dixon 2017). The Perspective classifier has been used in prior Reddit studies (Mittos et al 2020;Xia et al 2020). Research evaluating its performance on comments from political communities shows that, on average, its toxicity classification is comparable to a single human judgment of toxicity (Rajadesingan, Resnick, and Budak 2020).…”

Section: Quantifying Toxicity Of Repliesmentioning

confidence: 99%

Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)

Rajadesingan¹,

Budak²,

Resnick³

2021

Preprint

0

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Research on online political communication has primarily focused on content in explicitly political spaces. In this work, we set out to determine the amount of political talk missed using this approach. Focusing on Reddit, we estimate that nearly half of all political talk takes place in subreddits that host political content less than 25% of the time. In other words, cumulatively, political talk in non-political spaces is abundant. We further examine the nature of political talk and show that political conversations are less toxic in nonpolitical subreddits. Indeed, the average toxicity of political comments replying to a out-partisan in non-political subreddits is even less than the toxicity of co-partisan replies in explicitly political subreddits. IntroductionCasual everyday political conversations are central to a vibrant deliberative democracy. Through these conversations, individuals learn new perspectives, form informed opinions and update their preferences (Kim and Kim 2008). These interactions may take place in explicitly political spaces such as city townhalls and civic committees but also in seemingly non-political spaces such as book readings, workplaces and social gatherings (Conover and Miller 2018). Importantly, this kind of everyday political talk is significantly correlated with opinion quality and political participation which are central to forming a well-informed electorate (Wyatt, Katz, and Kim 2000). In this work, we explore this phenomenon online, particularly studying political discussions in communities on Reddit that are not explicitly political.Most research on political discussions has primarily focused on explicitly political spaces, examining communities around political news groups, figures or ideologies (Soliman, Hafer, and Lemmerich 2019;Himelboim, Gleave, and Smith 2009;An et al. 2019). However, survey research suggests that most people encounter political content online not in explicitly political spaces but in hobby and leisure groups where politics is incidental to the conversation (Wojcieszak and Mutz 2009). Further, recent years have seen increased political engagement among the electorate perhaps due to high levels of partisanship (Huddy, Mason, and Aarøe 2015) and growing social movements (Bosi, Giugni,

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“…Posts made within each subreddit must adhere to these guidelines or offending users may be banned from future posting or commenting in the subreddit. Due to the more relaxed moderation rules on the platform, especially surrounding controversial topics, Reddit threads commonly contain toxic, harassing, or explicit commentary [22]. A distinct difference that separates Reddit from Twitter or Facebook is how communities form.…”

Section: John Wilkes Boothmentioning

confidence: 99%

Reddit Entity Linking Dataset

Botzer,

Ding,

Weninger

2021

Preprint

0

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We introduce and make publicly available an entity linking dataset from Reddit that contains 17,316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement. We analyze the different errors and disagreements made by annotators and suggest three types of corrections to the raw data. Finally, we tested existing entity linking models that are trained and tuned on text from nonsocial media datasets. We find that, although these existing entity linking models perform very well on their original datasets, they perform poorly on this social media dataset. We also show that the majority of these errors can be attributed to poor performance on the mention detection subtask. These results indicate the need for better entity linking models that can be applied to the enormous amount of social media text.

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Analyzing Genetic Testing Discourse on the Web Through the Lens of Twitter, Reddit, and 4chan

Cited by 19 publications

References 39 publications

Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)

Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)

Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)

Reddit Entity Linking Dataset

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