Detecting Anti-vaccine Content on Twitter using Multiple Message-Based Network Representations
<|reference_start|>Detecting Anti-vaccine Content on Twitter using Multiple Message-Based Network Representations: Social media platforms such as Twitter have a fundamental role in facilitating the spread and discussion of ideas online through the concept of retweeting and replying. However, these features also contribute to the spread of mis/disinformation during the vaccine rollout of the COVID-19 pandemic. Using COVID-19 vaccines as a case study, we analyse multiple social network representation derived from three message-based interactions on Twitter (quote retweets, mentions and replies) based upon a set of known anti-vax hashtags and keywords. Each network represents a certain hashtag or keyword which were labelled as "controversial" and "non-controversial" according to a small group of participants. For each network, we extract a combination of global and local network-based metrics which are used as feature vectors for binary classification. Our results suggest that it is possible to detect controversial from non-controversial terms with high accuracy using simple network-based metrics. Furthermore, these results demonstrate the potential of network representations as language-agnostic models for detecting mis/disinformation at scale, irrespective of content and across multiple social media platforms.<|reference_end|>
@article{ashford2024detecting,
    title={Detecting Anti-vaccine Content on Twitter using Multiple Message-Based
  Network Representations},
    author={James R. Ashford},
    journal={arXiv preprint arXiv:2402.18335},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18335},
    primaryClass={cs.SI cs.IR}
}