A Robust Framework for Estimating Linguistic Alignment in Twitter Conversations

Gabriel Doyle, D. Yurovsky, Michael C. Frank
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引用次数: 29

Abstract

When people talk, they tend to adopt the behaviors, gestures, and language of their conversational partners. This "accommodation" to one's partners is largely automatic, but the degree to which it occurs is influenced by social factors, such as gender, relative power, and attraction. In settings where such social information is not known, this accommodation can be a useful cue for the missing information. This is especially important in web-based communication, where social dynamics are often fluid and rarely stated explicitly. But connecting accommodation and social dynamics on the web requires accurate quantification of the different amounts of accommodation being made. We focus specifically on accommodation in the form of "linguistic alignment": the amount that one person's word use is influenced by another's. Previous studies have used many measures for linguistic alignment, with no clear standard. In this paper, we lay out a set of desiderata for a linguistic alignment measure, including robustness to sparse and short messages, explicit conditionality, and consistency across linguistic features with different baseline frequencies. We propose a straightforward and flexible model-based framework for calculating linguistic alignment, with a focus on the sparse data and limited social information observed in social media. We show that this alignment measure fulfills our desiderata on simulated data. We then analyze a large corpus of Twitter data, both replicating previous results and extending them: Our measure's improved resolution reveals a previously undetectable effect of interpersonal power in Twitter interactions.
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推特对话中语言一致性评估的鲁棒框架
当人们交谈时,他们倾向于采用谈话对象的行为、手势和语言。这种对伴侣的“迁就”在很大程度上是自动的,但迁就的程度受到社会因素的影响,比如性别、相对权力和吸引力。在这种社会信息不为人所知的情况下,这种适应可能是对缺失信息的有用提示。这在基于网络的交流中尤其重要,因为社交动态往往是不稳定的,而且很少明确说明。但是,将网络上的住宿和社会动态联系起来,需要对不同数量的住宿进行精确的量化。我们特别关注“语言对齐”形式的适应:一个人的词汇使用受到另一个人的影响的程度。以前的研究使用了许多方法来进行语言对齐,但没有明确的标准。在本文中,我们列出了一组语言对齐度量所需的数据,包括对稀疏和短消息的鲁棒性,明确的条件,以及不同基线频率下语言特征的一致性。我们提出了一个简单而灵活的基于模型的框架来计算语言对齐,重点是在社交媒体中观察到的稀疏数据和有限的社会信息。我们在模拟数据上证明了这种对齐方法满足了我们的要求。然后,我们分析了大量的Twitter数据,既复制了之前的结果,又扩展了它们:我们的测量方法提高了分辨率,揭示了Twitter互动中人际权力以前无法察觉的影响。
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