Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter across Languages
- BATTELLE (PACIFIC NW LAB)
Social networks have an ephemerality to them where accounts and messages are constantly being edited, deleted, or marked as private. This continuous change comes from concerns around privacy, a potential desire for deception, and spam-like behavior. In this study we analyze multiple large datasets of thousands of active and deleted Twitter accounts to produce a series of predictive features for the removal or shutdown of an account. We have selected these accounts from speakers of three languages -- Russian, Spanish, and English to evaluate if speakers of various languages behave differently with regards to deleting accounts. We find that unlike previously used profile and network features, the discourse of deleted vs. active accounts forms the basis for highly accurate account deletion prediction. More precisely, we observed that the presence of a certain set of terms in user tweets leads to a higher likelihood for that user's account deletion. We show that the predictive power of profile, language, affect, and network features is not consistent across speakers of the three evaluated languages.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1765319
- Report Number(s):
- PNNL-SA-116993
- Resource Relation:
- Conference: Proceedings of the 11th International AAAI Conference on Web and Social Media (ICWSM 2017), May 15-18, 2017, Montreal, Canada
- Country of Publication:
- United States
- Language:
- English
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