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