2019
DOI: 10.1371/journal.pone.0216932
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The evolution of online ideological communities

Abstract: Online communities are virtual spaces for users to share interests, support others, and to exchange knowledge and information. Understanding user behavior is valuable to organizations and has applications from marketing to security, for instance, identifying leaders within a community or predicting future behavior. In the present research, we seek to understand the various roles that users adopt in online communities–for instance, who leads the conversation? Who are the supporters? We examine user role changes… Show more

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Cited by 24 publications
(7 citation statements)
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References 32 publications
(45 reference statements)
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“…Literature shows increased attention on the OHCs for computationally discovering patient health [21]. A majority of these studies follows a qualitative approach based on the manual categorization of posts by the domain experts.…”
Section: Related Workmentioning
confidence: 99%
“…Literature shows increased attention on the OHCs for computationally discovering patient health [21]. A majority of these studies follows a qualitative approach based on the manual categorization of posts by the domain experts.…”
Section: Related Workmentioning
confidence: 99%
“…Computational and social sciences are both deeply embedded within rich ethical traditions—traditions that are mutually informative. Drew (2016), for example, provides a brilliant overview of the ethical framework that grew from the Government Data Science Partnership, drawing from insights gained during deliberative public workshops and surveys. In her overview, Drew details principles such as data security, accountability, and articulation of public need/benefit—principles that are highly familiar to psychologists, but whose application to big data expands beyond traditional ethical research guidelines (Manzo & Brightbill, 2007; Sugiura, Wiles, & Pope, 2017).…”
Section: The Personality Panoramamentioning
confidence: 99%
“…In the world of big data, however, we find the explicit recognition that personality does not exist in a vacuum. In the digital realm, people do not merely behave, they interact with dynamic environmental factors that can also be measured-other people (Davidson, Jones, Joinson, & Hinds, 2019;Pan, Altshuler, & Pentland, 2012), games (Canossa, Badler, El-Nasr, Tignor, & Colvin, 2015), web pages (Shobeiri, Laroche, & Mazaheri, 2013;Turkyilmaz, Erdem, & Uslu, 2015), smartphone apps (Miller, 2012), and so on. Social context parameters such as network size, centrality (position or role), and local transitivity (connectedness among friends) are associated with personality (Fang et al, 2015;Gosling, Augustine, Vazire, Holtzman, & Gaddis, 2011;Staiano et al, 2012), and traits such as cooperation are at least in part emergent properties of context, dependent upon community structure (Apicella, Marlowe, Fowler, & Christakis, 2012).…”
Section: Behaviour Contextualizedmentioning
confidence: 99%
“…The distinction here would be: can we reliably and accurately distinguish between a mood fluctuation and a relapse or change in symptoms? (This is an issue seen in other computational social science considering distinctions between long term behavior changes or merely a temporary fluctuation50 ). Hence, starting this research within the general population to formulate a number of reliable metrics for mood, behavior, health outcome prediction is undoubtedly a safer route, which will provide findings that other areas of health and medicine can draw and build upon.Delving deeper into the realities and practicalities of digital phenotyping, there is a question asto what data is needed and how this data will be gathered.…”
mentioning
confidence: 99%
“…When obtaining data from a variety of sources, it is not uncommon to see research with >100 variables 4,55 being modelled to predict an outcome or mood severity, rather than focusing on few, but specific measures/metrics. By focusing on specific digital interactions and outcomes (e.g., behaviors, clinical outcomes of interest), this encourages 'behavioral analytics'26,50,56 , where we have underlying mechanisms or theories to understand why variable…”
mentioning
confidence: 99%