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arXiv 2407.19967cs.CL
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一种面向社交媒体用户身份解析的时间心理语言学方法

A Temporal Psycholinguistics Approach to Identity Resolution of Social Media Users

Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）

Md Touhidul Islam

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AI总结：

本文提出一种利用帖子主题、情感和时间进行跨社交媒体平台身份解析的方法，对比了时间与非时间方法，并实验了基于距离的奖惩评分模型，为后续多维度改进提供了方向。

摘要：

在本论文中，我们提出一种利用各平台帖子的主题、情感和时间进行跨社交媒体平台身份解析的方法。在收集了来自Disqus和Twitter约5000个个人资料的公开帖子后，我们分析这些帖子以匹配这两个平台上的资料。我们在分析中采用了时间与非时间两种方法。尽管两种方法均未表现出绝对的优越性，但时间方法通常表现更好。我们发现，时间窗口大小对结果的影响大于移动量。另一方面，我们的情感分析表明，纳入情感几乎没有产生差异，这可能是由于数据提取方法存在缺陷。我们还实验了一种基于距离的奖惩评分模型，该模型在我们收集的语料库中取得了24.198%的准确率，在2525个结果中的平均排名为158.217。未来的工作包括通过按主题评估情感来完善情感分析，利用额外阶段扩展时间分析，以及通过调整权重和修改奖励来改进评分模型。

英文摘要：

In this thesis, we propose an approach to identity resolution across social media platforms using the topics, sentiments, and timings of the posts on the platforms. After collecting the public posts of around 5000 profiles from Disqus and Twitter, we analyze their posts to match their profiles across the two platforms. We pursue both temporal and non-temporal methods in our analysis. While neither approach proves definitively superior, the temporal approach generally performs better. We found that the temporal window size influences results more than the shifting amount. On the other hand, our sentiment analysis shows that the inclusion of sentiment makes little difference, probably due to flawed data extraction methods. We also experimented with a distance-based reward-and-punishment-focused scoring model, which achieved an accuracy of 24.198% and an average rank of 158.217 out of 2525 in our collected corpus. Future work includes refining sentiment analysis by evaluating sentiments per topic, extending temporal analysis with additional phases, and improving the scoring model through weight adjustments and modified rewards.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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