We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners. The corpus consists of half a million instant messages, across several messaging platforms. We focus our analyses on seven speaker attributes, each of which partitions the set of speakers, namely: gender; relative age; family member; romantic partner; classmate; co-worker; and native to the same country. In addition to the content of the messages, we examine conversational aspects such as the time messages are sent, messaging frequency, psycholinguistic word categories, linguistic mirroring, and graph-based features reflecting how people in the corpus mention each other. We present two sets of experiments predicting each attribute using (1) short context windows; and (2) a larger set of messages. We find that using all features leads to gains of 9-14 over using message text only.
Discourse analysis approaches have been used to examine language to reveal social behavior patterns. Holmer @cite_0 applied discourse structure analysis to chat communication to identify and visualize message content and interaction structures. He focused on visualizing aspects such as conversation complexity, overlapping turns, distance between messages, turn changes, patterns in message production and references. In addition, he also proposed graph-based methods for showing coherence and thread patterns during the messaging interaction. Tuulos @cite_11 inferred social structures in chat-room conversations, using heuristics based on participants' references, message response time and dialog sequences and represented social structure using graph-based methods. Similarly, Jing @cite_4 looked at extracting networks of biographical facts from speech transcripts that characterize the relationships between people and organizations.
@cite_0 @cite_4 @cite_11
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This article presents a research method called Discourse Structure Analysis (DSA and a software application called ChatLine that supports the analysis of chat transcripts according to DSA. The DSA method is based on manual referencing and automatic analysis of chat transcripts in order to create visualizations and measures of their message and interaction structures. The goal of DSA is to provide a comprehensive and extensible method for the data‑driven analysis of chat logs that can support both qualitative and quantitative investigations of computer‑mediated communication. We present a general framework for automatically extracting social networks and biographical facts from conversational speech. Our approach relies on fusing the output produced by multiple information extraction modules, including entity recognition and detection, relation detection, and event detection modules. We describe the specific features and algorithmic refinements effective for conversational speech. These cumulatively increase the performance of social network extraction from 0.06 to 0.30 for the development set, and from 0.06 to 0.28 for the test set, as measured by f-measure on the ties within a network. The same framework can be applied to other genres of text — we have built an automatic biography generation system for general domain text using the same approach. Informal chat-room conversations have intrinsically different properties from regular static document collections. Noise, concise expressions and dynamic, changing and interleaving nature of discussions make chat data ill-suited for analysis with an off-the-shelf text mining method. On the other hand, interactive human communication has some implicit features which may be used to enhance the results. In our research we infer social network structures from the chat data by using a few basic heuristics. We then present some preliminary results showing that the inferred social graph may be used to enhance topic identification of a chat room when combined with a state-of-the-art topic and classification models. For validation purposes we then compare the performance effects of using this social information in a topic classification task.