Uncommon Yet Consequential Online Harms

Robertson, Ronald E.

doi:10.54501/jots.v1i3.87

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…Even accounting for the number of comments, this topic constitutes a minority (though non-trivial percentage) of the overall data in this study. However, these discussions could contribute to what Ronald E. Roberston has referred to as "uncommon yet consequential online harms" (Robertson, 2022). Based on reporting, discussions of suspicious persons could lead to lateral surveillance and offline police action with potentially disparate racialized impacts (Carman, 2020;Anthony, 2022).…”

Section: Discussionmentioning

confidence: 99%

Digital town square? Nextdoor's offline contexts and online discourse

Brown,

Sanderson,

Graham

et al. 2024

JQD

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There is scant quantitative research describing Nextdoor, the world's largest and most important hyperlocal social media network. Due to its localized structure, Nextdoor data are notoriously difficult to collect and work with. We build multiple datasets that allow us to generate descriptive analyses of the platform's offline contexts and online content. We first create a comprehensive dataset of all Nextdoor neighborhoods joined with U.S. Census data, which we analyze at the community-level (block-group). Our findings suggests that Nextdoor is primarily used in communities where the populations are whiter, more educated, more likely to own a home, and with higher levels of average income, potentially impacting the platform's ability to create new opportunities for social capital formation and citizen engagement. At the same time, Nextdoor neighborhoods are more likely to have active government agency accounts---and law enforcement agencies in particular---where offline communities are more urban, have larger nonwhite populations, greater income inequality, and higher average home values. We then build a convenience sample of 30 Nextdoor neighborhoods, for which we collect daily posts and comments appearing in the feed (115,716 posts and 163,903 comments), as well as associated metadata. Among the accounts for which we collected posts and comments, posts seeking or offering services were the most frequent, while those reporting potentially suspicious people or activities received the highest average number of comments. Taken together, our study describes the ecosystem of and discussion on Nextdoor, as well as introduces data for quantitatively studying the platform.

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Section: Discussionmentioning

confidence: 99%

Digital town square? Nextdoor's offline contexts and online discourse

Brown,

Sanderson,

Graham

et al. 2024

JQD

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“…Or conversely, community effects could overshadow and conceal the behavior of some user minorities. The former scenario is well documented in the literature about online harms, where it is typical for a minority of fringe users to be responsible for the majority of the harms (Zannettou et al 2020;Robertson 2022). Instead, the latter possibility relates to Foucault Welles' case for making Big Data small, in that platform and community effects could "silence [minorities and outliers] through statistical aggregation" (Foucault Welles 2014).…”

Section: Discussionmentioning

confidence: 99%

“…However, aggregated effects at these levels are the combination of many and potentially diverse effects at the user level. Hence, such aggregated effects might not be truly representative of the underlying behavior of individuals or smaller user groups (Foucault Welles 2014;Robertson 2022). Moreover, the same aggregated effect could be the result of multiple different distributions of user-level effects (Cresci, Di Pietro, and Tesconi 2019), each corresponding to a different practical situation.…”

Section: Introductionmentioning

confidence: 99%

Dataset — Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald

Trujillo,

Cresci

2022

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Evaluating the effects of moderation interventions is a task of paramount importance, as it allows assessing the success of content moderation processes. So far, intervention effects have been almost solely evaluated at the aggregated platform or community levels. Here, we carry out a multidimensional evaluation of the user level effects of the sequence of moderation interventions that targeted r/The Donald: a community of Donald Trump adherents on Reddit. We demonstrate that the interventions (i) strongly reduced user activity, (ii) slightly increased the diversity of the subreddits in which users participated, (iii) slightly reduced user toxicity, and (iv) led users to share less factual and more politically biased news. Importantly, we also find that interventions having strong community level effects also cause extreme and diversified user level reactions. Our results highlight that platform and community level effects are not always representative of the underlying behavior of individuals or smaller user groups. We conclude by discussing the practical and ethical implications of our results. Overall, our findings can inform the development of targeted moderation interventions and provide useful guidance for policing online platforms.

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“…Table 1 also reports the percentages of malicious and genuine users, and of their tweets, with respect to the total users and tweets in our datasets. This highlights the large imbalance between genuine and malicious users, which is representative of the reality of OSNs [61], [62]. Since the malicious users are removed from Twitter/X, the collected datasets do not include direct interactions (e.g., retweets) between genuine and malicious users.…”

Section: Remarksmentioning

confidence: 99%

Coordinated Behavior in Information Operations on Twitter

Cima,

Mannocci,

Avvenuti

et al. 2024

IEEE Access

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Online information operations (IOs) refer to organized attempts to tamper with the regular flow of information and to influence public opinion. Coordinated online behavior is a tactic frequently used by IO perpetrators to boost the spread and outreach of their messages. However, the exploitation of coordinated behavior within large-scale IOs is still largely unexplored. Here, we build a novel dataset comprising around 624K users and 4M tweets to study how online coordination was used in two recent IOs carried out on Twitter. We investigate the interplay between coordinated behavior and IOs with state-of-the-art network science and coordination detection methods, providing evidence that the perpetrators of both IOs were indeed strongly coordinated. Furthermore, we propose quantitative indicators and analyses to study the different patterns of coordination, uncovering a malicious group of users that managed to hold a central position in the discussion network, and others who remained at the periphery of the network, with limited interactions with genuine users. The nuanced results enabled by our analysis provide insights into the strategies, development, and effectiveness of the IOs. Overall, our results demonstrate that the analysis of coordinated behavior in IOs can contribute to safeguarding the integrity of online platforms.

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Uncommon Yet Consequential Online Harms

Cited by 6 publications

References 17 publications

Digital town square? Nextdoor's offline contexts and online discourse

Digital town square? Nextdoor's offline contexts and online discourse

Dataset — Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald

Coordinated Behavior in Information Operations on Twitter

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