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Register a new userWhy Did They #Unfollow Me? Early Detection of Follower Loss on Twitter
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Added by
Suman Kalyan Maity
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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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).
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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-called troll accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter itself. We construct and analyse a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques, along with a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts and we find that one troll account appears in the top-100 and four in the top-1000. This combined with other findings presented in this paper constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000 respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election.
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 Australian skies. This expansive size has enabled us to capture a majority of the atmospheric trajectory of a spectacular grazing event that lasted over90 seconds, penetrated as deep as ~58.5km, and traveled over 1,300 km through the atmosphere before exiting back into interplanetary space. Based on our triangulation and dynamic analyses of the event, we have estimated the initial mass to be at least 60 kg, which would correspond to a30 cm object given a chondritic density (3500 kg m-3). However, this initial mass estimate is likely a lower bound, considering the minimal deceleration observed in the luminous phase. The most intriguing quality of this close encounter is that the meteoroid originated from an Apollo-type orbit and was inserted into a Jupiter-family comet (JFC) orbit due to the net energy gained during the close encounter with the Earth. Based on numerical simulations, the meteoroid will likely spend ~200kyrs on a JFC orbit and have numerous encounters with Jupiter, the first of which will occur in January-March 2025. Eventually the meteoroid will likely be ejected from the Solar System or be flung into a trans-Neptunian orbit.
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 modern methods shows that the data used by Halley do not contain significant evidence for proper motion. What Halley found are the measurement errors of Ptolemaios and Brahe. Halley further argued that the occultation of Aldebaran by the Moon on 11 March 509 in Athens confirmed the change in latitude of Aldebaran. In fact, however, the relevant observation was almost certainly made in Alexandria where Aldebaran was not occulted. By carefully considering measurement errors Jacques Cassini showed that Halleys results from comparison with earlier astronomers were spurious, a conclusion partially confirmed by various later authors. Cassinis careful study of the measurements of the latitude of Arcturus provides the first significant evidence for proper motion.
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 parents (the causal mechanisms), we attribute the change to changes of these causal mechanisms. This attribution analysis accounts for the fact that mechanisms often change independently and sometimes only some of them change. Through simulations, we study the performance of our distribution change attribution method. We then present a real-world case study identifying the drivers of the difference in the income distribution between men and women.
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 datasets. However, existing benchmarks generally suffer from low levels of user diversity, limited user information and data scarcity. Therefore, these datasets are not sufficient to train and stably benchmark bot detection measures. To alleviate these problems, we present TwiBot-20, a massive Twitter bot detection benchmark, which contains 229,573 users, 33,488,192 tweets, 8,723,736 user property items and 455,958 follow relationships. TwiBot-20 covers diversified bots and genuine users to better represent the real-world Twittersphere. TwiBot-20 also includes three modals of user information to support both binary classification of single users and community-aware approaches. To the best of our knowledge, TwiBot-20 is the largest Twitter bot detection benchmark to date. We reproduce competitive bot detection methods and conduct a thorough evaluation on TwiBot-20 and two other public datasets. Experiment results demonstrate that existing bot detection measures fail to match their previously claimed performance on TwiBot-20, which suggests that Twitter bot detection remains a challenging task and requires further research efforts.
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