Assessing the bias in samples of large online networks

González‐Bailón, Sandra; Wang, Ning; Rivero, Alejandro; Borge-Holthoefer, Javier; Moreno, Yamir

doi:10.1016/j.socnet.2014.01.004

Cited by 196 publications

(151 citation statements)

References 36 publications

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“…In fact, data obtained from a simple backward search tend to over-represents more central users, not offering an accurate picture of peripheral activity, with more relevant bias for the network of mentions [14]. Therefore, we used the streaming Application Programming Interface (API) made available by Twitter, to collect all messages posted on the social network satisfying a set of temporal and semantic constraints.…”

Section: Methodsmentioning

confidence: 99%

“…It has been shown that individuals tend to share ties within the same metropolitan region and that nonlocal ties distance, borders and language differences affect their relationships [8]. Many studies were devoted to determine which and how information flows through the network [9][10][11][12], as well as to understand the mechanisms of information spreading-e.g., as in the case of viral content-to identify influential spreaders and comprehend their role [13][14][15][16][17]. Attention has also been given to investigate social dynamics during emergence of protests [18], with evidences of social influence and complex contagion providing an empirical test to the recruitment mechanisms theorized in formal models of collective action [19].…”

Section: Introductionmentioning

confidence: 99%

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Characterizing interactions in online social networks during exceptional events

2015

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Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing interactions in online social networks during exceptional events

2015

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“…There is, however, also reason for scepticism. Commercial platforms such as Facebook and Twitter do not provide their APIs as a service to researchers, but have other uses which inhibit reproducible sampling and frequently render data sets incomplete (González-Bailón et al 2014;Gerlitz & Rieder 2013). Capturing data from such platforms is also computationally resource-intensive, imposing limitations on research.…”

Section: Reliability Validity and Representativeness Of Api Datamentioning

confidence: 99%

The datafied society, studying culture through data

Morsello

2017

Information, Communication & Society

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“…Thus, the rising interest in digital media and social interactions mediated by online technologies is boosting the research outputs in an emerging field that is multidisciplinary by nature: it brings together computer scientists, sociologists, physicists, and researchers from a wide range of other disciplines [15].…”

Section: Introductionmentioning

confidence: 99%

From neuroscience to computer science: a topical approach on Twitter

et al. 2017

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Twitter is perhaps the most influential microblogging service, with 271 million regular users producing approximately 500 million tweets per day. Previous studies of tweets discussing scientific topics are limited to local surveys or may not be representative geographically. This indicates a need to harvest and analyse tweets with the aim of understanding the level of dissemination of science related topics worldwide. In this study, we use Twitter as case of study and explore the hypothesis of science popularization via the social stream. We present and discuss tweets related to popular science around the world using eleven keywords. We analyze a sample of 306,163 tweets posted by 91,557 users with the aim of identifying tweets and those categories formed around temporally similar topics. We systematically examined the data to track and analyze the online activity around users tweeting about popular science. In addition, we identify locations of high Twitter activity that occur in several places around the world. We also examine which sources (mobile devices, apps, and other social networks) are used to share popular science related links. Furthermore, this study provides insights into the geographic density of popular science tweets worldwide. We show that emergent topics related to popular -017-0002-9 science are important because they could help to explore how science becomes of public interest. The study also offers some important insights for studying the type of scientific content that users are more likely to tweet.

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Assessing the bias in samples of large online networks

Cited by 196 publications

References 36 publications

Characterizing interactions in online social networks during exceptional events

Characterizing interactions in online social networks during exceptional events

The datafied society, studying culture through data

From neuroscience to computer science: a topical approach on Twitter

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