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Having more followers has become a norm in recent social media and micro-blogging communities. This battle has been taking shape from the early days of Twitter. Despite this strong competition for followers, many Twitter users are continuously losing their followers. This work addresses the problem of identifying the reasons behind the drop of followers of users in Twitter. As a first step, we extract various features by analyzing the content of the posts made by the Twitter users who lose followers consistently. We then leverage these features to early detect follower loss. We propose various models and yield an overall accuracy of 73% with high precision and recall. Our model outperforms baseline model by 19.67% (w.r.t accuracy), 33.8% (w.r.t precision) and 14.3% (w.r.t recall).
It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-
For centuries extremely-long grazing fireball displays have fascinated observers and inspired people to ponder about their origins. The Desert Fireball Network (DFN) is the largest single fireball network in the world, covering about one third of Aus
In 1717 Halley compared contemporaneous measurements of the latitudes of four stars with earlier measurements by ancient Greek astronomers and by Brahe, and from the differences concluded that these four stars showed proper motion. An analysis with m
We describe a formal approach based on graphical causal models to identify the root causes of the change in the probability distribution of variables. After factorizing the joint distribution into conditional distributions of each variable, given its
Twitter has become a vital social media platform while an ample amount of malicious Twitter bots exist and induce undesirable social effects. Successful Twitter bot detection proposals are generally supervised, which rely heavily on large-scale datas