采用基于方面的情感分析和 GDM 共识度量法评估回音室

Q1 Social Sciences Online Social Networks and Media Pub Date : 2024-01-01 DOI:10.1016/j.osnem.2024.100276
Miriam Amendola , Danilo Cavaliere , Carmen De Maio , Giuseppe Fenza , Vincenzo Loia
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引用次数: 0

摘要

在社交网络上自然会出现回音室,个人加入群组分享和讨论自己的兴趣爱好,而算法会根据个人的情绪、偏见和认知弱点引导他们的信念和行为。根据最近对信息操纵和干扰的研究,回声室已成为认知战武器库中的重要武器,可放大心理技术的效果,从而改变信息和叙述,影响公众的看法和意见。研究的重点是确定评估方法,以检测新出现的回声室并监测其随时间的演变。从这个意义上说,这项工作强调了现有的基于拓扑结构的衡量标准和群体及其所属用户的观点语义的互补作用。事实上,本文提出了一种基于共识的群体决策(GDM)度量方法,该方法通过基于方面的情感分析(ABSA)获取群体成员的观点,并应用共识度量方法来确定单个群体内部以及不同群体之间的一致性。通过与情感感知意见分析以及最先进的极化和回音室检测指标进行比较,我们在两个公共推文数据集上评估了所提指标的潜力。结果表明,基于拓扑结构的指标严格依赖于对个体的随机游走,不足以充分描述社区在主题上的接近程度以及内容分析得出的普遍观点。
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Towards echo chamber assessment by employing aspect-based sentiment analysis and GDM consensus metrics

Echo chambers naturally occur on social networks, where individuals join groups to share and discuss their own interests driven by algorithms that steer their beliefs and behaviours based on their emotions, biases, and cognitive vulnerabilities. According to recent research on information manipulation and interference, echo chambers have become crucial weapons in the arsenal of Cognitive Warfare for amplifying the effect of psychological techniques aimed at altering information and narratives to influence public perception and shape opinions. The research is focusing on the definition of assessment methods for detecting emerging echo chambers and monitoring their evolution over time. In this sense, this work stresses the complementary role of the existing topology-based metrics and the semantics of the viewpoints underlying groups as well as their belonging users. Indeed, this paper proposes a metric based on consensus Group Decision-Making (GDM) that acquires community members’ opinions through Aspect-Based Sentiment Analysis (ABSA) and applies consensus metrics to determine the agreement within a single community and between distinct communities. The potential of the proposed metrics have been evaluated on two public datasets of tweets through comparisons with sentiment-aware opinions analysis and state-of-the-art metrics for polarization and echo chamber detection. The results reveal that topology-based metrics strictly depending on random walks over the individuals are not sufficient to fully depict the communities closeness on topics and their prevailing beliefs coming out from content analysis.

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来源期刊
Online Social Networks and Media
Online Social Networks and Media Social Sciences-Communication
CiteScore
10.60
自引率
0.00%
发文量
32
审稿时长
44 days
期刊最新文献
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