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The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads on-line. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles across blog sites, Facebook, news sites, Twitter, and Wikipedia. To do so, we present a burst-based framework for examining online discussions about science over time and across different platforms. To measure information retention, we develop a keyword-based computational measure comparing an online post to the scientific article’s abstract. We evaluate our measure using ground truth data labeled by within field experts. We highlight three main findings: first, we find a strong tendency towards low levels of information retention, following a distinct trajectory of loss except when bursts of attention begin in social media. Second, platforms show significant differences in information retention. Third, sequences involving more platforms tend to be associated with higher information retention. These findings highlight a strong tendency towards information loss over time—posing a critical concern for researchers, policymakers, and citizens alike—but suggest that multi-platform discussions may im-prove information retention overall. 

The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads online. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles across blog sites, Facebook, news sites, Twitter, and Wikipedia. To do so, we present a burst-based framework for examining online discussions about science over time and across different platforms. To measure information retention, we develop a keyword-based computational measure comparing an online post to the scientific article's abstract. We evaluate our measure using ground truth data labeled by within field experts. We highlight three main findings: first, we find a strong tendency towards low levels of information retention, following a distinct trajectory of loss except when bursts of attention begin in social media. Second, platforms show significant differences in information retention. Third, sequences involving more platforms tend to be associated with higher information retention. These findings highlight a strong tendency towards information loss over time---posing a critical concern for researchers, policymakers, and citizens alike---but suggest that multi-platform discussions may improve information retention overall.

 

The governance of many online communities relies on rules created by participants. However, prior work provides limited evidence about how these self-governance efforts compare and relate to one another across communities. Studies tend either to analyze communities as discrete entities or consider communities that coexist within a hierarchically-managed platform. In this paper, we investigate both comparative and relational dimensions of self-governance in similar communities. We use exhaustive trace data from the five largest language editions of Wikipedia over almost 20 years since their founding, and consider both patterns in rule-making and overlaps in rule sets. We find similar rule-making activity across the five communities that replicates and extends prior work on English language Wikipedia alone. However, we also find that these Wikipedias have increasingly unique rule sets, even as editing activity concentrates on rules shared between them. Self-governing communities aligned in key ways may share a common core of rules and rule-making practices as they develop and sustain institutional variations. 

Many benefits of online communities---such as obtaining new information, opportunities, and social connections---increase with size. Thus, a "successful'' online community often evokes an image of hundreds of thousands of users, and practitioners and researchers alike have sought to devise methods to achieve growth and thereby, success. On the other hand, small online communities exist in droves and many persist in their smallness over time. Turning to the highly popular discussion website Reddit, which is made up of hundreds of thousands of communities, we conducted a qualitative interview study examining how and why people participate in these persistently small communities, in order to understand why these communities exist when popular approaches would assume them to be failures. Drawing from twenty interviews, this paper makes several contributions: we describe how small communities provide unique informational and interactional spaces for participants, who are drawn by the hyperspecific aspects of the community; we find that small communities do not promote strong dyadic interpersonal relationships but rather promote group-based identity; and we highlight how participation in small communities is part of a broader, ongoing strategy to curate participants' online experience. We argue that online communities can be seen as nested niches: parts of an embedded, complex, symbiotic socio-informational ecosystem. We suggest ways that social computing research could benefit from more deliberate considerations of interdependence between diverse scales of online community sizes.