  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.
