Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter

By James McCorriston, David Jurgens, Derek Ruths. 2015

66 citations recorded by the source corpus.

Much work on the demographics of social media platforms such as Twitter has focused on the properties of individuals, such as gender or age. However, because credible detectors for organization accounts do not exist, these and future large-scale studies of human behavior on social media can be contaminated by the presence of accounts belonging to organizations. We analyze organizations on Twitter to assess their distinct behavioral characteristics and determine what types of organizations are active. We first create a dataset of manually classified accounts from a representative sample of Twitter and then introduce a classifier to distinguish between organizational and personal accounts. In addition, we find that although organizations make up less than 10% of the accounts, they are significantly more connected, with an order of magnitude more friends and followers.

Subfields: Communication, Information Systems, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Researchers have noted that “studies of human behavior on social media can be contaminated by the presence of accounts belonging to organizations” (McCorriston et al., 2015).”
Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2016, background): “However, gender ambiguous or unknown users may be organizational accounts that tend to have more followers than individuals (McCorriston, Jurgens, and Ruths 2015).”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “, training a classifier to detect organization accounts [66], or to apply existing natural language processing techniques that carefully resolve lexical ambiguities by e.”
The Geography and Importance of Localness in Geotagged Social Media (2016, methodology): “For all Twitter datasets, we remove organizational accounts per best practices for social media research [37] prior to analysis through use of the classifier described by McCorriston et al. [30].”
Data ex Machina: Introduction to Big Data (2017, background): “Many organizations and institutions are present and active in these systems as well, including corporations, national states, and terrorist organizations (McCorriston et al. 2015).”