111 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
Characterizing Geo-located Tweets in Brazilian Megacities
This work presents a framework for collecting, processing and mining
geo-located tweets in order to extract meaningful and actionable knowledge in
the context of smart cities. We collected and characterized more than 9M tweets
from the two biggest cities in Brazil, Rio de Janeiro and S\~ao Paulo. We
performed topic modeling using the Latent Dirichlet Allocation model to produce
an unsupervised distribution of semantic topics over the stream of geo-located
tweets as well as a distribution of words over those topics. We manually
labeled and aggregated similar topics obtaining a total of 29 different topics
across both cities. Results showed similarities in the majority of topics for
both cities, reflecting similar interests and concerns among the population of
Rio de Janeiro and S\~ao Paulo. Nevertheless, some specific topics are more
predominant in one of the cities
Characterizing Geo-located Tweets in Brazilian Megacities
This work presents a framework for collecting, processing and mining
geo-located tweets in order to extract meaningful and actionable knowledge in
the context of smart cities. We collected and characterized more than 9M tweets
from the two biggest cities in Brazil, Rio de Janeiro and S\~ao Paulo. We
performed topic modeling using the Latent Dirichlet Allocation model to produce
an unsupervised distribution of semantic topics over the stream of geo-located
tweets as well as a distribution of words over those topics. We manually
labeled and aggregated similar topics obtaining a total of 29 different topics
across both cities. Results showed similarities in the majority of topics for
both cities, reflecting similar interests and concerns among the population of
Rio de Janeiro and S\~ao Paulo. Nevertheless, some specific topics are more
predominant in one of the cities
Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling
Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue
and Zika in Brasil and other tropical regions has long been a priority for
governments in affected areas. Streaming social media content, such as Twitter,
is increasingly being used for health vigilance applications such as flu
detection. However, previous work has not addressed the complexity of drastic
seasonal changes on Twitter content across multiple epidemic outbreaks. In
order to address this gap, this paper contrasts two complementary approaches to
detecting Twitter content that is relevant for Dengue outbreak detection,
namely supervised classification and unsupervised clustering using topic
modelling. Each approach has benefits and shortcomings. Our classifier achieves
a prediction accuracy of about 80\% based on a small training set of about
1,000 instances, but the need for manual annotation makes it hard to track
seasonal changes in the nature of the epidemics, such as the emergence of new
types of virus in certain geographical locations. In contrast, LDA-based topic
modelling scales well, generating cohesive and well-separated clusters from
larger samples. While clusters can be easily re-generated following changes in
epidemics, however, this approach makes it hard to clearly segregate relevant
tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano,
Switzerlan
Timeline Generation: Tracking individuals on Twitter
In this paper, we propose a unsupervised framework to reconstruct a person's
life history by creating a chronological list for {\it personal important
events} (PIE) of individuals based on the tweets they published. By analyzing
individual tweet collections, we find that what are suitable for inclusion in
the personal timeline should be tweets talking about personal (as opposed to
public) and time-specific (as opposed to time-general) topics. To further
extract these types of topics, we introduce a non-parametric multi-level
Dirichlet Process model to recognize four types of tweets: personal
time-specific (PersonTS), personal time-general (PersonTG), public
time-specific (PublicTS) and public time-general (PublicTG) topics, which, in
turn, are used for further personal event extraction and timeline generation.
To the best of our knowledge, this is the first work focused on the generation
of timeline for individuals from twitter data. For evaluation, we have built a
new golden standard Timelines based on Twitter and Wikipedia that contain PIE
related events from 20 {\it ordinary twitter users} and 20 {\it celebrities}.
Experiments on real Twitter data quantitatively demonstrate the effectiveness
of our approach
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