23,074 research outputs found
Sentiment and Authority Analysis in Conversational Content
This paper deals with mining conversational content from the social media. It focused on two issues: opinion and emotion classification and identification of authoritative reviewers. The paper also describes applications representing the results obtained in the given areas. Authority identification can be used by organizations to search for experts in their specific areas to employ them. The opinion and emotion analysis can be useful for providing decision-making support
User Intent Prediction in Information-seeking Conversations
Conversational assistants are being progressively adopted by the general
population. However, they are not capable of handling complicated
information-seeking tasks that involve multiple turns of information exchange.
Due to the limited communication bandwidth in conversational search, it is
important for conversational assistants to accurately detect and predict user
intent in information-seeking conversations. In this paper, we investigate two
aspects of user intent prediction in an information-seeking setting. First, we
extract features based on the content, structural, and sentiment
characteristics of a given utterance, and use classic machine learning methods
to perform user intent prediction. We then conduct an in-depth feature
importance analysis to identify key features in this prediction task. We find
that structural features contribute most to the prediction performance. Given
this finding, we construct neural classifiers to incorporate context
information and achieve better performance without feature engineering. Our
findings can provide insights into the important factors and effective methods
of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201
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An overview study of twitter in the UK local government
Copyright @ 2012 Brunel UniversityMicroblogging applications are becoming a momentous element of the public sector social media agenda. The potential of Twitter to update the public with frequent, concise and real-time content has motivated many pubic authorities to create their accounts, thus generating an interesting topic for research. This paper seeks to make an empirical and methodological contribution to this new body of knowledge by presenting an overview study of general Twitter accounts maintained by UK local government authorities. Over 296,000 tweets were collected from the 187officially listed local government accounts. The analysis was conducted in two stages: an examination of the Twitter networks developed by the accounts was followed by a structural analysis of the tweets. The combination of online research and social media analytics techniques enabled us to reach important conclusions about the use of Twitter by those authorities. The findings indicate high level of maturity of Twitter in the UK local government and point to several directions for further increasing the impact and visibility of those accounts within a social media strategy
A virtual diary companion
Chatbots and embodied conversational agents show turn based conversation behaviour. In current research we almost always assume that each utterance of a human conversational partner should be followed by an intelligent and/or empathetic reaction of chatbot or embodied agent. They are assumed to be alert, trying to please the user. There are other applications which have not yet received much attention and which require a more patient or relaxed attitude, waiting for the right moment to provide feedback to the human partner. Being able and willing to listen is one of the conditions for being successful. In this paper we have some observations on listening behaviour research and introduce one of our applications, the virtual diary companion
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Twitter in local government: A study of Greater London authorities
Copyright @ 2011 SIG eGOVMicroblogging services are considered an emerging opportunity for authorities seeking to establish new communication channels with their public. Potential benefits evolve around enhancing transparency and interactivity, as well as sharing information regularly or during emergency events. The purpose of this exploratory study is to advance our empirical understanding of microblogging in local government. In particular, we reflect on online data collected to profile the use of Twitter by 29 Greater London local authorities (LAs). The study shows that London LAs have been accumulating significant experience with Twitter mainly over the past two years. In fact, many of them appear to incorporate conversational characteristics in their Tweets other than simply disseminating information. Furthermore, an analysis of Tweets during the August 2011 riots in England indicates the usefulness of the medium for responsibly informing the public and preventing rumours. Nevertheless, the study also identifies several points of improvement in the way public authorities are building their online networks; for example, in terms of connecting with each other and exploiting even more the conversational characteristics of Twitter
Graph-based Features for Automatic Online Abuse Detection
While online communities have become increasingly important over the years,
the moderation of user-generated content is still performed mostly manually.
Automating this task is an important step in reducing the financial cost
associated with moderation, but the majority of automated approaches strictly
based on message content are highly vulnerable to intentional obfuscation. In
this paper, we discuss methods for extracting conversational networks based on
raw multi-participant chat logs, and we study the contribution of graph
features to a classification system that aims to determine if a given message
is abusive. The conversational graph-based system yields unexpectedly high
performance , with results comparable to those previously obtained with a
content-based approach
Linguistic Markers of Influence in Informal Interactions
There has been a long standing interest in understanding `Social Influence'
both in Social Sciences and in Computational Linguistics. In this paper, we
present a novel approach to study and measure interpersonal influence in daily
interactions. Motivated by the basic principles of influence, we attempt to
identify indicative linguistic features of the posts in an online knitting
community. We present the scheme used to operationalize and label the posts
with indicator features. Experiments with the identified features show an
improvement in the classification accuracy of influence by 3.15%. Our results
illustrate the important correlation between the characteristics of the
language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for
Computational Linguistics) 201
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