2 research outputs found
Event detection in Colombian security Twitter news using fine-grained latent topic analysis
Cultural and social dynamics are important concepts that must be understood
in order to grasp what a community cares about. To that end, an excellent
source of information on what occurs in a community is the news, especially in
recent years, when mass media giants use social networks to communicate and
interact with their audience. In this work, we use a method to discover latent
topics in tweets from Colombian Twitter news accounts in order to identify the
most prominent events in the country. We pay particular attention to security,
violence and crime-related tweets because of the violent environment that
surrounds Colombian society. The latent topic discovery method that we use
builds vector representations of the tweets by using FastText and finds
clusters of tweets through the K-means clustering algorithm. The number of
clusters is found by measuring the coherence for a range of number of
topics of the Latent Dirichlet Allocation (LDA) model. We finally use Uniform
Manifold Approximation and Projection (UMAP) for dimensionality reduction to
visualise the tweets vectors. Once the clusters related to security, violence
and crime are identified, we proceed to apply the same method within each
cluster to perform a fine-grained analysis in which specific events mentioned
in the news are grouped together. Our method is able to discover event-specific
sets of news, which is the baseline to perform an extensive analysis of how
people engage in Twitter threads on the different types of news, with an
emphasis on security, violence and crime-related tweets.Comment: pre-print exposed at CATAI (Bogot\'a, Colombia
Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs
This paper illustrates five different techniques to assess the
distinctiveness of topics, key terms and features, speed of information
dissemination, and network behaviors for Covid19 tweets. First, we use pattern
matching and second, topic modeling through Latent Dirichlet Allocation (LDA)
to generate twenty different topics that discuss case spread, healthcare
workers, and personal protective equipment (PPE). One topic specific to U.S.
cases would start to uptick immediately after live White House Coronavirus Task
Force briefings, implying that many Twitter users are paying attention to
government announcements. We contribute machine learning methods not previously
reported in the Covid19 Twitter literature. This includes our third method,
Uniform Manifold Approximation and Projection (UMAP), that identifies unique
clustering-behavior of distinct topics to improve our understanding of
important themes in the corpus and help assess the quality of generated topics.
Fourth, we calculated retweeting times to understand how fast information about
Covid19 propagates on Twitter. Our analysis indicates that the median
retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours,
approximately 50 minutes faster than repostings from Chinese social media about
H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by
visualizing the connections of users over time from fast to slow retweeting. As
the time to retweet increases, the density of connections also increase where
in our sample, we found distinct users dominating the attention of Covid19
retweeters. One of the simplest highlights of this analysis is that early-stage
descriptive methods like regular expressions can successfully identify
high-level themes which were consistently verified as important through every
subsequent analysis