A multidisciplinary framework for deconstructing bots' pluripotency in dualistic antagonism
<|reference_start|>A multidisciplinary framework for deconstructing bots' pluripotency in dualistic antagonism: Anthropomorphic social bots are engineered to emulate human verbal communication and generate toxic or inflammatory content across social networking services (SNSs). Bot-disseminated misinformation could subtly yet profoundly reshape societal processes by complexly interweaving factors like repeated disinformation exposure, amplified political polarization, compromised indicators of democratic health, shifted perceptions of national identity, propagation of false social norms, and manipulation of collective memory over time. However, extrapolating bots' pluripotency across hybridized, multilingual, and heterogeneous media ecologies from isolated SNS analyses remains largely unknown, underscoring the need for a comprehensive framework to characterise bots' emergent risks to civic discourse. Here we propose an interdisciplinary framework to characterise bots' pluripotency, incorporating quantification of influence, network dynamics monitoring, and interlingual feature analysis. When applied to the geopolitical discourse around the Russo-Ukrainian conflict, results from interlanguage toxicity profiling and network analysis elucidated spatiotemporal trajectories of pro-Russian and pro-Ukrainian human and bots across hybrid SNSs. Weaponized bots predominantly inhabited X, while human primarily populated Reddit in the social media warfare. This rigorous framework promises to elucidate interlingual homogeneity and heterogeneity in bots' pluripotent behaviours, revealing synergistic human-bot mechanisms underlying regimes of information manipulation, echo chamber formation, and collective memory manifestation in algorithmically structured societies.<|reference_end|>
@article{xu2024a,
    title={A multidisciplinary framework for deconstructing bots' pluripotency in
  dualistic antagonism},
    author={Wentao Xu, Kazutoshi Sasahara, Jianxun Chu, Bin Wang, Wenlu Fan,
  Zhiwen Hu},
    journal={arXiv preprint arXiv:2402.15119},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15119},
    primaryClass={cs.CY cs.SI}
}