12,716 research outputs found
Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools
While detecting and interpreting temporal patterns of nonâverbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and humanâcentered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement
Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
Research on the structure of dialogue has been hampered for years because
large dialogue corpora have not been available. This has impacted the dialogue
research community's ability to develop better theories, as well as good off
the shelf tools for dialogue processing. Happily, an increasing amount of
information and opinion exchange occur in natural dialogue in online forums,
where people share their opinions about a vast range of topics. In particular
we are interested in rejection in dialogue, also called disagreement and
denial, where the size of available dialogue corpora, for the first time,
offers an opportunity to empirically test theoretical accounts of the
expression and inference of rejection in dialogue. In this paper, we test
whether topic-independent features motivated by theoretical predictions can be
used to recognize rejection in online forums in a topic independent way. Our
results show that our theoretically motivated features achieve 66% accuracy, an
improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent
Identification of Agreement and Disagreement in Social Media Dialogue},
author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference},
year={2013}
Computational Controversy
Climate change, vaccination, abortion, Trump: Many topics are surrounded by
fierce controversies. The nature of such heated debates and their elements have
been studied extensively in the social science literature. More recently,
various computational approaches to controversy analysis have appeared, using
new data sources such as Wikipedia, which help us now better understand these
phenomena. However, compared to what social sciences have discovered about such
debates, the existing computational approaches mostly focus on just a few of
the many important aspects around the concept of controversies. In order to
link the two strands, we provide and evaluate here a controversy model that is
both, rooted in the findings of the social science literature and at the same
time strongly linked to computational methods. We show how this model can lead
to computational controversy analytics that have full coverage over all the
crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social
Informatics (SocInfo) 201
Predicting continuous conflict perception with Bayesian Gaussian processes
Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach
that detects common conversational social signals (loudness, overlapping speech,
etc.) and predicts the conflict level perceived by human observers in continuous,
non-categorical terms. The proposed regression approach is fully Bayesian and it
adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
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