9,458 research outputs found
Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses
Systems that exploit publicly available user generated content such as
Twitter messages have been successful in tracking seasonal influenza. We
developed a novel filtering method for Influenza-Like-Illnesses (ILI)-related
messages using 587 million messages from Twitter micro-blogs. We first filtered
messages based on syndrome keywords from the BioCaster Ontology, an extant
knowledge model of laymen's terms. We then filtered the messages according to
semantic features such as negation, hashtags, emoticons, humor and geography.
The data covered 36 weeks for the US 2009 influenza season from 30th August
2009 to 8th May 2010. Results showed that our system achieved the highest
Pearson correlation coefficient of 98.46% (p-value<2.2e-16), an improvement of
3.98% over the previous state-of-the-art method. The results indicate that
simple NLP-based enhancements to existing approaches to mine Twitter data can
increase the value of this inexpensive resource.Comment: 10 pages, 5 figures, IEEE HISB 2012 conference, Sept 27-28, 2012, La
Jolla, California, U
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
The Multimodal Sentiment Analysis Challenge (MuSe) 2022 is dedicated to
multimodal sentiment and emotion recognition. For this year's challenge, we
feature three datasets: (i) the Passau Spontaneous Football Coach Humor
(Passau-SFCH) dataset that contains audio-visual recordings of German football
coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in
which reactions of individuals to emotional stimuli have been annotated with
respect to seven emotional expression intensities, and (iii) the Ulm-Trier
Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled
with continuous emotion values (arousal and valence) of people in stressful
dispositions. Using the introduced datasets, MuSe 2022 2022 addresses three
contemporary affective computing problems: in the Humor Detection Sub-Challenge
(MuSe-Humor), spontaneous humour has to be recognised; in the Emotional
Reactions Sub-Challenge (MuSe-Reaction), seven fine-grained `in-the-wild'
emotions have to be predicted; and in the Emotional Stress Sub-Challenge
(MuSe-Stress), a continuous prediction of stressed emotion values is featured.
The challenge is designed to attract different research communities,
encouraging a fusion of their disciplines. Mainly, MuSe 2022 targets the
communities of audio-visual emotion recognition, health informatics, and
symbolic sentiment analysis. This baseline paper describes the datasets as well
as the feature sets extracted from them. A recurrent neural network with LSTM
cells is used to set competitive baseline results on the test partitions for
each sub-challenge. We report an Area Under the Curve (AUC) of .8480 for
MuSe-Humor; .2801 mean (from 7-classes) Pearson's Correlations Coefficient for
MuSe-Reaction, as well as .4931 Concordance Correlation Coefficient (CCC) and
.4761 for valence and arousal in MuSe-Stress, respectively.Comment: Preliminary baseline paper for the 3rd Multimodal Sentiment Analysis
Challenge (MuSe) 2022, a full-day workshop at ACM Multimedia 202
Darwin’s neuroscientist: Gerald M. Edelman, 1929-2014
No description supplie
Facts and Fabrications about Ebola: A Twitter Based Study
Microblogging websites like Twitter have been shown to be immensely useful
for spreading information on a global scale within seconds. The detrimental
effect, however, of such platforms is that misinformation and rumors are also
as likely to spread on the network as credible, verified information. From a
public health standpoint, the spread of misinformation creates unnecessary
panic for the public. We recently witnessed several such scenarios during the
outbreak of Ebola in 2014 [14, 1]. In order to effectively counter the medical
misinformation in a timely manner, our goal here is to study the nature of such
misinformation and rumors in the United States during fall 2014 when a handful
of Ebola cases were confirmed in North America. It is a well known convention
on Twitter to use hashtags to give context to a Twitter message (a tweet). In
this study, we collected approximately 47M tweets from the Twitter streaming
API related to Ebola. Based on hashtags, we propose a method to classify the
tweets into two sets: credible and speculative. We analyze these two sets and
study how they differ in terms of a number of features extracted from the
Twitter API. In conclusion, we infer several interesting differences between
the two sets. We outline further potential directions to using this material
for monitoring and separating speculative tweets from credible ones, to enable
improved public health information.Comment: Appears in SIGKDD BigCHat Workshop 201
Interactions of Organizational Culture and Collaboration in Working and Learning
This paper reports the methodologies and findings of research done into learning processes in two diverse environments. The research focused on identifying factors that enable and facilitate social learning. These factors are discussed in view of the preliminary architecture and in view of the sociotechnical environment within people work and learn. The paper concludes by suggesting that the development of information system requires an understanding of the cultural and interpersonal issues prevalent in work environments.Knowledge Management, Organizational culture, Organizational learning, Socio-tehnical approach
Report on the Information Retrieval Festival (IRFest2017)
The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017
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