32,707 research outputs found
Collective emotions online and their influence on community life
E-communities, social groups interacting online, have recently become an
object of interdisciplinary research. As with face-to-face meetings, Internet
exchanges may not only include factual information but also emotional
information - how participants feel about the subject discussed or other group
members. Emotions are known to be important in affecting interaction partners
in offline communication in many ways. Could emotions in Internet exchanges
affect others and systematically influence quantitative and qualitative aspects
of the trajectory of e-communities? The development of automatic sentiment
analysis has made large scale emotion detection and analysis possible using
text messages collected from the web. It is not clear if emotions in
e-communities primarily derive from individual group members' personalities or
if they result from intra-group interactions, and whether they influence group
activities. We show the collective character of affective phenomena on a large
scale as observed in 4 million posts downloaded from Blogs, Digg and BBC
forums. To test whether the emotions of a community member may influence the
emotions of others, posts were grouped into clusters of messages with similar
emotional valences. The frequency of long clusters was much higher than it
would be if emotions occurred at random. Distributions for cluster lengths can
be explained by preferential processes because conditional probabilities for
consecutive messages grow as a power law with cluster length. For BBC forum
threads, average discussion lengths were higher for larger values of absolute
average emotional valence in the first ten comments and the average amount of
emotion in messages fell during discussions. Our results prove that collective
emotional states can be created and modulated via Internet communication and
that emotional expressiveness is the fuel that sustains some e-communities.Comment: 23 pages including Supporting Information, accepted to PLoS ON
Internet source evaluation: The role of implicit associations and psychophysiological self-regulation
This study focused on middle school students\u2019 source evaluation skills as a key component of digital literacy. Specifically, it examined the role of two unexplored individual factors that may affect the evaluation of sources providing information about the controversial topic of the health risks associated with the use of mobile phones. The factors were the implicit association of mobile phone with health or no health, and psychophysiological self-regulation as reflected in basal Heart Rate Variability (HRV). Seventy-two seventh graders read six webpages that provided contrasting information on the unsettled topic of the potential health risks related to the use of mobile phones. Then they were asked to rank-order the six websites along the dimension of reliability (source evaluation). Findings revealed that students were able to discriminate between the most and least reliable websites, justifying their ranking in light of different criteria. However, overall, they were little accurate in rank-ordering all six Internet sources. Both implicit associations and HRV correlated with source evaluation. The interaction between the two individual variables was a significant predictor of participants\u2019 performance in rank-ordering the websites for reliability. A slope analysis revealed that when students had an average psychophysiological self-regulation, the stronger their association of the mobile phone with health, the better their performance on source evaluation. Theoretical and educational significances of the study are discussed
Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies
Stories can have tremendous power -- not only useful for entertainment, they
can activate our interests and mobilize our actions. The degree to which a
story resonates with its audience may be in part reflected in the emotional
journey it takes the audience upon. In this paper, we use machine learning
methods to construct emotional arcs in movies, calculate families of arcs, and
demonstrate the ability for certain arcs to predict audience engagement. The
system is applied to Hollywood films and high quality shorts found on the web.
We begin by using deep convolutional neural networks for audio and visual
sentiment analysis. These models are trained on both new and existing
large-scale datasets, after which they can be used to compute separate audio
and visual emotional arcs. We then crowdsource annotations for 30-second video
clips extracted from highs and lows in the arcs in order to assess the
micro-level precision of the system, with precision measured in terms of
agreement in polarity between the system's predictions and annotators' ratings.
These annotations are also used to combine the audio and visual predictions.
Next, we look at macro-level characterizations of movies by investigating
whether there exist `universal shapes' of emotional arcs. In particular, we
develop a clustering approach to discover distinct classes of emotional arcs.
Finally, we show on a sample corpus of short web videos that certain emotional
arcs are statistically significant predictors of the number of comments a video
receives. These results suggest that the emotional arcs learned by our approach
successfully represent macroscopic aspects of a video story that drive audience
engagement. Such machine understanding could be used to predict audience
reactions to video stories, ultimately improving our ability as storytellers to
communicate with each other.Comment: Data Mining (ICDM), 2017 IEEE 17th International Conference o
xDelia final report: emotion-centred financial decision making and learning
xDelia is a 3-year pan-European project building on the knowledge, skills, and competences of seven partner organisations from a variety of research disciplines and from business. The principal objective of xDelia is to develop technology-enhanced learning approaches that help improve the financial decision making of investors who trade frequently using an electronic trading platform. We focus on emotions, and how they affect maladaptive decision biases and trading performance. Our earlier field work with traders has shown that the development of emotion regulation skills is a key facet of trader expertise. For that reason we consider expert traders our benchmark for adaptive behaviour rather than normative rationality. Our goal is to provide investors with the tools and techniques to develop greater self-awareness of internal states, increase their ability to reflect critically on emotion-informed choices, develop emotion management skills, and support the transfer of these skills to the real-world practice setting of financial trading.
This report provides a comprehensive overview of what xDelia is about and what we have achieved over the life of the project. In the sections that follow, we explain the decision problems investors are faced with in a fast paced environment and the limitations of traditional approaches to reduce cognitive errors; introduce an alternative, technology-enhanced learning approach of diagnosis and feedback, skill development, and transfer; describe the learning intervention comprising twelve autonomous learning elements that we have developed; and present evidence from thirty-five studies we have conducted on learning effects and stakeholder acceptance
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while
incorporating knowledge about the user into the conversation. In this work we
address the acquisition of such knowledge, for personalization in downstream
Web applications, by extracting personal attributes from conversations. This
problem is more challenging than the established task of information extraction
from scientific publications or Wikipedia articles, because dialogues often
give merely implicit cues about the speaker. We propose methods for inferring
personal attributes, such as profession, age or family status, from
conversations using deep learning. Specifically, we propose several Hidden
Attribute Models, which are neural networks leveraging attention mechanisms and
embeddings. Our methods are trained on a per-predicate basis to output rankings
of object values for a given subject-predicate combination (e.g., ranking the
doctor and nurse professions high when speakers talk about patients, emergency
rooms, etc). Experiments with various conversational texts including Reddit
discussions, movie scripts and a collection of crowdsourced personal dialogues
demonstrate the viability of our methods and their superior performance
compared to state-of-the-art baselines.Comment: published in WWW'1
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