38,317 research outputs found
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Every culture and language is unique. Our work expressly focuses on the
uniqueness of culture and language in relation to human affect, specifically
sentiment and emotion semantics, and how they manifest in social multimedia. We
develop sets of sentiment- and emotion-polarized visual concepts by adapting
semantic structures called adjective-noun pairs, originally introduced by Borth
et al. (2013), but in a multilingual context. We propose a new
language-dependent method for automatic discovery of these adjective-noun
constructs. We show how this pipeline can be applied on a social multimedia
platform for the creation of a large-scale multilingual visual sentiment
concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our
unified ontology is organized hierarchically by multilingual clusters of
visually detectable nouns and subclusters of emotionally biased versions of
these nouns. In addition, we present an image-based prediction task to show how
generalizable language-specific models are in a multilingual context. A new,
publicly available dataset of >15.6K sentiment-biased visual concepts across 12
languages with language-specific detector banks, >7.36M images and their
metadata is also released.Comment: 11 pages, to appear at ACM MM'1
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
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