InterSpot: Interactive Spammer Detection in Social Media

Kaize Ding, Jundong Li, Shivam Dhar, Shreyash Devan, Huan Liu

IJCAI 2019 pp. 6509-6511

doi:10.24963/IJCAI.2019/939 /ijcai/2019/ding2019ijcai-interspot/

Abstract

Spammer detection in social media has recently received increasing attention due to the rocketing growth of user-generated data. Despite the empirical success of existing systems, spammers may continuously evolve over time to impersonate normal users while new types of spammers may also emerge to combat with the current detection system, leading to the fact that a built system will gradually lose its efficacy in spotting spammers. To address this issue, grounded on the contextual bandit model, we present a novel system for conducting interactive spammer detection. We demonstrate our system by showcasing the interactive learning process, which allows the detection model to keep optimizing its detection strategy through incorporating the feedback information from human experts.

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Cite

Text

Ding et al. "InterSpot: Interactive Spammer Detection in Social Media." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/939

Markdown

[Ding et al. "InterSpot: Interactive Spammer Detection in Social Media." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/ding2019ijcai-interspot/) doi:10.24963/IJCAI.2019/939

BibTeX

@inproceedings{ding2019ijcai-interspot,
  title     = {{InterSpot: Interactive Spammer Detection in Social Media}},
  author    = {Ding, Kaize and Li, Jundong and Dhar, Shivam and Devan, Shreyash and Liu, Huan},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {6509-6511},
  doi       = {10.24963/IJCAI.2019/939},
  url       = {https://mlanthology.org/ijcai/2019/ding2019ijcai-interspot/}
}