38 research outputs found
Mathematical Modeling of Trending Topics on Twitter
Created in 2006, Twitter is an online social networking service in which users share and read 140-character messages called Tweets. The site has approximately 288 million monthly active users who produce about 500 million Tweets per day. This study applies dynamical and statistical modeling strategies to quantify the spread of information on Twitter. Parameter estimates for the rates of infection and recovery are obtained using Bayesian Markov Chain Monte Carlo (MCMC) methods. The methodological strategy employed is an extension of techniques traditionally used in an epidemiological and biomedical context (particularly in the spread of infectious disease). This study, which addresses information spread, presents case studies pertaining to the prevalence of several “trending” topics on Twitter over time. The study introduces a framework to compare information dynamics on Twitter based on the topical area as well as a framework for the prediction of topic prevalence. Additionally, methodological and results-based comparisons are drawn between the spread of information and the spread of infectious disease
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
Social networking websites allow users to create and share content. Big
information cascades of post resharing can form as users of these sites reshare
others' posts with their friends and followers. One of the central challenges
in understanding such cascading behaviors is in forecasting information
outbreaks, where a single post becomes widely popular by being reshared by many
users. In this paper, we focus on predicting the final number of reshares of a
given post. We build on the theory of self-exciting point processes to develop
a statistical model that allows us to make accurate predictions. Our model
requires no training or expensive feature engineering. It results in a simple
and efficiently computable formula that allows us to answer questions, in
real-time, such as: Given a post's resharing history so far, what is our
current estimate of its final number of reshares? Is the post resharing cascade
past the initial stage of explosive growth? And, which posts will be the most
reshared in the future? We validate our model using one month of complete
Twitter data and demonstrate a strong improvement in predictive accuracy over
existing approaches. Our model gives only 15% relative error in predicting
final size of an average information cascade after observing it for just one
hour.Comment: 10 pages, published in KDD 201
Get Out of the Nest! Drivers of Social Influence in the #TwitterMigration to Mastodon
The migration of Twitter users to Mastodon following Elon Musk's acquisition
presents a unique opportunity to study collective behavior and gain insights
into the drivers of coordinated behavior in online media. We analyzed the
social network and the public conversations of about 75,000 migrated users and
observed that the temporal trace of their migrations is compatible with a
phenomenon of social influence, as described by a compartmental epidemic model
of information diffusion. Drawing from prior research on behavioral change, we
delved into the factors that account for variations across different Twitter
communities in the effectiveness of the spreading of the influence to migrate.
Communities in which the influence process unfolded more rapidly exhibit lower
density of social connections, higher levels of signaled commitment to
migrating, and more emphasis on shared identity and exchange of factual
knowledge in the community discussion. These factors account collectively for
57% of the variance in the observed data. Our results highlight the joint
importance of network structure, commitment, and psycho-linguistic aspects of
social interactions in describing grassroots collective action, and contribute
to deepen our understanding of the mechanisms driving processes of behavior
change of online groups
How Infectious is Your Twitter Feed? Disease Modeling Applied to the Dynamics of Twitter
This study aims to use compartmental disease-models to explore Twitter dynamics. Applying an epidemiology model to Twitter tweets can give deeper insights into the factors that make a tweet go viral. In addition, this study explored the differences between a stochastic and a dynamic compartmental model. This research connects the world of diseases with the internet and explored if a disease model will accurately model Twitter dynamics. We found that stochastic models were better at fitting to smaller populations of data than dynamic models were. Dynamic models ended up predicting larger populations better. Furthermore, we found that although a topic is popular does not mean that it is infectious. This study was able to show that disease modeling is able to accurately predict Twitter dynamics