6,220 research outputs found
Is That Twitter Hashtag Worth Reading
Online social media such as Twitter, Facebook, Wikis and Linkedin have made a
great impact on the way we consume information in our day to day life. Now it
has become increasingly important that we come across appropriate content from
the social media to avoid information explosion. In case of Twitter, popular
information can be tracked using hashtags. Studying the characteristics of
tweets containing hashtags becomes important for a number of tasks, such as
breaking news detection, personalized message recommendation, friends
recommendation, and sentiment analysis among others.
In this paper, we have analyzed Twitter data based on trending hashtags,
which is widely used nowadays. We have used event based hashtags to know users'
thoughts on those events and to decide whether the rest of the users might find
it interesting or not. We have used topic modeling, which reveals the hidden
thematic structure of the documents (tweets in this case) in addition to
sentiment analysis in exploring and summarizing the content of the documents. A
technique to find the interestingness of event based twitter hashtag and the
associated sentiment has been proposed. The proposed technique helps twitter
follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium
on Women in Computing and Informatics (WCI-2015
Viewpoint Discovery and Understanding in Social Networks
The Web has evolved to a dominant platform where everyone has the opportunity
to express their opinions, to interact with other users, and to debate on
emerging events happening around the world. On the one hand, this has enabled
the presence of different viewpoints and opinions about a - usually
controversial - topic (like Brexit), but at the same time, it has led to
phenomena like media bias, echo chambers and filter bubbles, where users are
exposed to only one point of view on the same topic. Therefore, there is the
need for methods that are able to detect and explain the different viewpoints.
In this paper, we propose a graph partitioning method that exploits social
interactions to enable the discovery of different communities (representing
different viewpoints) discussing about a controversial topic in a social
network like Twitter. To explain the discovered viewpoints, we describe a
method, called Iterative Rank Difference (IRD), which allows detecting
descriptive terms that characterize the different viewpoints as well as
understanding how a specific term is related to a viewpoint (by detecting other
related descriptive terms). The results of an experimental evaluation showed
that our approach outperforms state-of-the-art methods on viewpoint discovery,
while a qualitative analysis of the proposed IRD method on three different
controversial topics showed that IRD provides comprehensive and deep
representations of the different viewpoints
Real-Time Classification of Twitter Trends
Social media users give rise to social trends as they share about common
interests, which can be triggered by different reasons. In this work, we
explore the types of triggers that spark trends on Twitter, introducing a
typology with following four types: 'news', 'ongoing events', 'memes', and
'commemoratives'. While previous research has analyzed trending topics in a
long term, we look at the earliest tweets that produce a trend, with the aim of
categorizing trends early on. This would allow to provide a filtered subset of
trends to end users. We analyze and experiment with a set of straightforward
language-independent features based on the social spread of trends to
categorize them into the introduced typology. Our method provides an efficient
way to accurately categorize trending topics without need of external data,
enabling news organizations to discover breaking news in real-time, or to
quickly identify viral memes that might enrich marketing decisions, among
others. The analysis of social features also reveals patterns associated with
each type of trend, such as tweets about ongoing events being shorter as many
were likely sent from mobile devices, or memes having more retweets originating
from a few trend-setters.Comment: Pre-print of article accepted for publication in Journal of the
American Society for Information Science and Technology copyright @ 2013
(American Society for Information Science and Technology
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