Abstract

Various socio-political organizations, from activist groups to propaganda campaigners, create accounts on Twitter to reach out, influence and gain followers. In order to analyze the impact of these organizational accounts, the first step is to identify them. In this paper, we develop and experiment with a set of network-based, behavioral, temporal and spatial characteristics in these accounts, independent of domain or language, to identify features that can be useful in detecting organizational accounts. In order to assess this model, we experimented with a microblog corpus comprised of over 7 million tweets from 150,000 Twitter users in Bangladesh, tweeted between June and October 2016. We sampled 31,139 accounts using cold-start heuristics to locate and label nearly 200 organizational accounts, distributed as 68 NGOs, 62 news outlets, 35 political groups, and 17 public intellectual and iconic figures. The remaining accounts were labeled as individuals. Next, we developed a set of features and experimented with a set of linear and non-linear classifiers. The highest performing sparse logistic regression classifier achieved an accuracy of 68.2% precision and 64.4% recall leading to a 66.2% F1-score in detecting less than 1% rare organizational accounts using a set of content- and language-independent features.

Original languageEnglish (US)
Title of host publicationSocial, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings
EditorsHalil Bisgin, Robert Thomson, Ayaz Hyder, Christopher Dancy
PublisherSpringer Verlag
Pages164-175
Number of pages12
ISBN (Print)9783319933719
DOIs
StatePublished - Jan 1 2018
Event11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018 - Washington, United States
Duration: Jul 10 2018Jul 13 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10899 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018
CountryUnited States
CityWashington
Period7/10/187/13/18

Keywords

  • Automatic identification
  • Social network
  • Social network analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Finding organizational accounts based on structural and behavioral factors on twitter'. Together they form a unique fingerprint.

  • Cite this

    Alzahrani, S., Gore, C., Salehi, A., & Davulcu, H. (2018). Finding organizational accounts based on structural and behavioral factors on twitter. In H. Bisgin, R. Thomson, A. Hyder, & C. Dancy (Eds.), Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings (pp. 164-175). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10899 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93372-6_18