3,908 research outputs found
Extracting News Events from Microblogs
Twitter stream has become a large source of information for many people, but
the magnitude of tweets and the noisy nature of its content have made
harvesting the knowledge from Twitter a challenging task for researchers for a
long time. Aiming at overcoming some of the main challenges of extracting the
hidden information from tweet streams, this work proposes a new approach for
real-time detection of news events from the Twitter stream. We divide our
approach into three steps. The first step is to use a neural network or deep
learning to detect news-relevant tweets from the stream. The second step is to
apply a novel streaming data clustering algorithm to the detected news tweets
to form news events. The third and final step is to rank the detected events
based on the size of the event clusters and growth speed of the tweet
frequencies. We evaluate the proposed system on a large, publicly available
corpus of annotated news events from Twitter. As part of the evaluation, we
compare our approach with a related state-of-the-art solution. Overall, our
experiments and user-based evaluation show that our approach on detecting
current (real) news events delivers a state-of-the-art performance
Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
In contrast to much previous work that has focused on location classification
of tweets restricted to a specific country, here we undertake the task in a
broader context by classifying global tweets at the country level, which is so
far unexplored in a real-time scenario. We analyse the extent to which a
tweet's country of origin can be determined by making use of eight
tweet-inherent features for classification. Furthermore, we use two datasets,
collected a year apart from each other, to analyse the extent to which a model
trained from historical tweets can still be leveraged for classification of new
tweets. With classification experiments on all 217 countries in our datasets,
as well as on the top 25 countries, we offer some insights into the best use of
tweet-inherent features for an accurate country-level classification of tweets.
We find that the use of a single feature, such as the use of tweet content
alone -- the most widely used feature in previous work -- leaves much to be
desired. Choosing an appropriate combination of both tweet content and metadata
can actually lead to substantial improvements of between 20\% and 50\%. We
observe that tweet content, the user's self-reported location and the user's
real name, all of which are inherent in a tweet and available in a real-time
scenario, are particularly useful to determine the country of origin. We also
experiment on the applicability of a model trained on historical tweets to
classify new tweets, finding that the choice of a particular combination of
features whose utility does not fade over time can actually lead to comparable
performance, avoiding the need to retrain. However, the difficulty of achieving
accurate classification increases slightly for countries with multiple
commonalities, especially for English and Spanish speaking countries.Comment: Accepted for publication in IEEE Transactions on Knowledge and Data
Engineering (IEEE TKDE
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
#Santiago is not #Chile, or is it? A Model to Normalize Social Media Impact
Online social networks are known to be demographically biased. Currently
there are questions about what degree of representativity of the physical
population they have, and how population biases impact user-generated content.
In this paper we focus on centralism, a problem affecting Chile. Assuming that
local differences exist in a country, in terms of vocabulary, we built a
methodology based on the vector space model to find distinctive content from
different locations, and use it to create classifiers to predict whether the
content of a micro-post is related to a particular location, having in mind a
geographically diverse selection of micro-posts. We evaluate them in a case
study where we analyze the virtual population of Chile that participated in the
Twitter social network during an event of national relevance: the municipal
(local governments) elections held in 2012. We observe that the participating
virtual population is spatially representative of the physical population,
implying that there is centralism in Twitter. Our classifiers out-perform a non
geographically-diverse baseline at the regional level, and have the same
accuracy at a provincial level. However, our approach makes assumptions that
need to be tested in multi-thematic and more general datasets. We leave this
for future work.Comment: Accepted in ChileCHI 2013, I Chilean Conference on Human-Computer
Interactio
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