Backfire Effect Reveals Early Controversy in Online Media
<|reference_start|>Backfire Effect Reveals Early Controversy in Online Media: The rapid development of online media has significantly facilitated the public's information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has primarily focused on larger datasets and more complex computational models but has rarely examined the underlying mechanisms of conflict, particularly the psychological motivations behind them. In this paper, we present evidence that conflicting posts tend to have a high proportion of "ascending gradient of likes", i.e., replies get more likes than comments. Additionally, there is a gradient in the number of replies between the neighboring tiers as well. We develop two new gradient features and demonstrate the common enhancement effect of our features in terms of controversy detection models. Further, multiple evaluation algorithms are used to compare structural, interactive, and textual features with the new features across multiple Chinese and English media. The results show that it is a general case that gradient features are significantly different in terms of controversy and are more important than other features. More thoroughly, we discuss the mechanism by which the ascending gradient emerges, suggesting that the case is related to the "backfire effect" in ideological conflicts that have received recent attention. The features formed by the psychological mechanism also show excellent detection performance in application scenarios where only a few hot information or early information are considered. Our findings can provide a new perspective for online conflict behavior analysis and early detection.<|reference_end|>
@article{peng2024backfire,
    title={Backfire Effect Reveals Early Controversy in Online Media},
    author={Songtao Peng, Chenbo Fua, Han Han, Ye Wu, Kailun Zhu, Qi Xuan, Yong
  Min},
    journal={arXiv preprint arXiv:2403.02708},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02708},
    primaryClass={cs.SI}
}