17,761 research outputs found
Summarizing Dialogic Arguments from Social Media
Online argumentative dialog is a rich source of information on popular
beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary should take. Previous work on summarization has primarily focused on
summarizing written texts, where the notion of an abstract of the text is well
defined. We collect gold standard training data consisting of five human
summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control
and Abortion. We present several different computational models aimed at
identifying segments of the dialogues whose content should be used for the
summary, using linguistic features and Word2vec features with both SVMs and
Bidirectional LSTMs. We show that we can identify the most important arguments
by using the dialog context with a best F-measure of 0.74 for gun control, 0.71
for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of
Dialogue (SemDial 2017
Implementing Web 2.0 in secondary schools: impacts, barriers and issues
One of the reports from the Web 2.0 technologies for learning at KS3 and KS4 project. This report explored Impact of Web 2.0 technologies on learning and teaching and drew upon evidence from multiple sources: field studies of 27 schools across the country; guided surveys of 2,600 school students; 100 interviews and 206 online surveys conducted with managers, teachers and technical staff in these schools; online surveys of the views of 96 parents; interviews held with 18 individual innovators in the field of Web 2.0 in education; and interviews with nine regional managers responsible for implementation of ICT at national level
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
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A rule dynamics approach to event detection in Twitter with its application to sports and politics
The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events
When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter
Assessing political conversations in social media requires a deeper
understanding of the underlying practices and styles that drive these
conversations. In this paper, we present a computational approach for assessing
online conversational practices of political parties. Following a deductive
approach, we devise a number of quantitative measures from a discussion of
theoretical constructs in sociological theory. The resulting measures make
different - mostly qualitative - aspects of online conversational practices
amenable to computation. We evaluate our computational approach by applying it
in a case study. In particular, we study online conversational practices of
German politicians on Twitter during the German federal election 2013. We find
that political parties share some interesting patterns of behavior, but also
exhibit some unique and interesting idiosyncrasies. Our work sheds light on (i)
how complex cultural phenomena such as online conversational practices are
amenable to quantification and (ii) the way social media such as Twitter are
utilized by political parties.Comment: 10 pages, 2 figures, 3 tables, Proc. 8th International AAAI
Conference on Weblogs and Social Media (ICWSM 2014
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