8,399 research outputs found
What Words Do We Use to Lie?: Word Choice in Deceptive Messages
Text messaging is the most widely used form of computer- mediated
communication (CMC). Previous findings have shown that linguistic factors can
reliably indicate messages as deceptive. For example, users take longer and use
more words to craft deceptive messages than they do truthful messages. Existing
research has also examined how factors, such as student status and gender,
affect rates of deception and word choice in deceptive messages. However, this
research has been limited by small sample sizes and has returned contradicting
findings. This paper aims to address these issues by using a dataset of text
messages collected from a large and varied set of participants using an Android
messaging application. The results of this paper show significant differences
in word choice and frequency of deceptive messages between male and female
participants, as well as between students and non-students
An Army of Me: Sockpuppets in Online Discussion Communities
In online discussion communities, users can interact and share information
and opinions on a wide variety of topics. However, some users may create
multiple identities, or sockpuppets, and engage in undesired behavior by
deceiving others or manipulating discussions. In this work, we study
sockpuppetry across nine discussion communities, and show that sockpuppets
differ from ordinary users in terms of their posting behavior, linguistic
traits, as well as social network structure. Sockpuppets tend to start fewer
discussions, write shorter posts, use more personal pronouns such as "I", and
have more clustered ego-networks. Further, pairs of sockpuppets controlled by
the same individual are more likely to interact on the same discussion at the
same time than pairs of ordinary users. Our analysis suggests a taxonomy of
deceptive behavior in discussion communities. Pairs of sockpuppets can vary in
their deceptiveness, i.e., whether they pretend to be different users, or their
supportiveness, i.e., if they support arguments of other sockpuppets controlled
by the same user. We apply these findings to a series of prediction tasks,
notably, to identify whether a pair of accounts belongs to the same underlying
user or not. Altogether, this work presents a data-driven view of deception in
online discussion communities and paves the way towards the automatic detection
of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
Interpersonal relations are fickle, with close friendships often dissolving
into enmity. In this work, we explore linguistic cues that presage such
transitions by studying dyadic interactions in an online strategy game where
players form alliances and break those alliances through betrayal. We
characterize friendships that are unlikely to last and examine temporal
patterns that foretell betrayal.
We reveal that subtle signs of imminent betrayal are encoded in the
conversational patterns of the dyad, even if the victim is not aware of the
relationship's fate. In particular, we find that lasting friendships exhibit a
form of balance that manifests itself through language. In contrast, sudden
changes in the balance of certain conversational attributes---such as positive
sentiment, politeness, or focus on future planning---signal impending betrayal.Comment: To appear at ACL 2015. 10pp, 4 fig. Data and other info available at
http://vene.ro/betrayal
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