TY - GEN
T1 - Finding organizational accounts based on structural and behavioral factors on twitter
AU - Alzahrani, Sultan
AU - Gore, Chinmay
AU - Salehi, Amin
AU - Davulcu, Hasan
N1 - Funding Information:
Acknowledgements. This work was supported by the Office of Naval Research under Grant No.: N00014-16-1-2015, and N00014-15-1-2722.
Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Automatic identification
KW - Social network
KW - Social network analysis
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U2 - 10.1007/978-3-319-93372-6_18
DO - 10.1007/978-3-319-93372-6_18
M3 - Conference contribution
AN - SCOPUS:85049801841
SN - 9783319933719
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 175
BT - Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings
A2 - Bisgin, Halil
A2 - Thomson, Robert
A2 - Hyder, Ayaz
A2 - Dancy, Christopher
PB - Springer Verlag
T2 - 11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018
Y2 - 10 July 2018 through 13 July 2018
ER -