NDLI: Actions speak as loud as words: predicting relationships from social behavior data

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Actions speak as loud as words: predicting relationships from social behavior data

Content Provider	ACM Digital Library
Author	Sisenda, Fred Magdon-Ismail, Malik Adali, Sibel
Abstract	In recent years, new studies concentrating on analyzing user personality and finding credible content in social media have become quite popular. Most such work augments features from textual content with features representing the user's social ties and the tie strength. Social ties are crucial in understanding the network the people are a part of. However, textual content is extremely useful in understanding topics discussed and the personality of the individual. We bring a new dimension to this type of analysis with methods to compute the type of ties individuals have and the strength of the ties in each dimension. We present a new genre of behavioral features that are able to capture the "function" of a specific relationship without the help of textual features. Our novel features are based on the statistical properties of communication patterns between individuals such as reciprocity, assortativity, attention and latency. We introduce a new methodology for determining how such features can be compared to textual features, and show, using Twitter data, that our features can be used to capture contextual information present in textual features very accurately. Conversely, we also demonstrate how textual features can be used to determine social attributes related to an individual.
Starting Page	689
Ending Page	698
Page Count	10
File Format	PDF
ISBN	9781450312295
DOI	10.1145/2187836.2187930
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social signals Social ties Behavior analysis Social networks
Content Type	Text
Resource Type	Article

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