An Effective Approach for Modelling Time Features for Classifying Bursty Topics on Twitter

Several previous approaches attempted to predict bursty topics on Twitter. Such approaches have usually reported that the time information (e.g. the topic popularity over time) of hashtag topics contribute the most to the prediction of bursty topics. In this paper, we propose a novel approach to use time features to predict bursty topics on Twitter. We model the popularity of topics as density curves described by the density function of a beta distribution with different parameters. We then propose various approaches to predict/classify the bursty topics by estimating the parameters of topics, using estimators such as Gradient Decent or Likelihood Maximization. In our experiments, we show that the estimated parameters of topics have a positive effect on classifying bursty topics. In particular, our estimators when combined together improve the bursty topic classification by 6.9 in terms of micro F1 compared to a baseline classifier using hashtag content features

Anatomy of Viral Social Media Events

Discussion topics go sometimes viral in social media without a seemingly coherent pattern. Existing literature shows these discussions can reach a very high level, but, notably, they prevail to varying degrees. This paper investigates the anatomy of viral social media events using a dataset of 960 viral social media discussion topics that have been identified by an algorithm from a variety of social media sources over two years’ time. A negative binomial regression shows that the average daily amount and the relative change in the daily amount of social media platforms at which the event has been discussed has a positive effect on the duration of the event. Average or relative amount of posts or authors has no or very little effect on event duration. The results suggest that viral social media events last longer when people using different social media platforms get exposed to them. This finding contributes to the literature on social media events, virality, and information diffusion.Peer reviewe

Spotting Icebergs by the Tips: Rumor and Persuasion Campaign Detection in Social Media

Identifying different types of events in social media, i.e., collective online activities or posts, is critical for researchers who study data mining and online communication. However, the online activities of more than one billion social media users from around the world constitute an ocean of data that is hard to study and understand. In this dissertation, we study the problem of event detection with a focus on two important applications---rumor and persuasion campaign detection. Detecting events such as rumors and persuasion campaigns is particularly important for social media users and researchers. Events in social media spread and influence people much more quickly than traditional news media reporting. Viral spreading of specific events, such as rumors and persuasion campaigns, can cause substantial damage in online communities. Automatic detection of these can benefit analysts in many different research domains. In this thesis, we extend the existing research on social media event detection of online events such as rumors and persuasion campaigns. We conducted content analysis and found that the emergence and spreading of certain types of online events often result in similar user reactions. For example, some users will react to the spreading of a rumor by questioning its truth, even though most posts will not explicitly question it. These explicit questions serve as signals for detecting the underlying events. Our approach to detecting a given type of event first identifies the signals from the myriad of posts in the data corpus. We then use these signals to find the rest of the targeted events. Different types of events have different signals. As case studies, we analyze and identify the signals for rumors and persuasion campaigns, and we apply our proposed framework to detect these two types of events. We began by analyzing large-scale online activities in order to understand the relation between events and their signals. We focused on detecting and analyzing users' question-asking activities. We found that many social media users react to popular and fast-emerging memes by explicitly asking questions. Compared to other user activities, these questions are more likely to be correlated to bursty events and emergent information needs. We use some of our findings to detect trending rumors. We find that in the case of rumors, a common reaction regardless of the content of the rumor is to question the truth of the statement. We use these questioning activities as signals for detecting rumors. Our experimental results show that our rumor detector can effectively and efficiently detect social media rumors at an early stage. As in the case of rumors, the emergence and spreading of persuasion campaigns can result in similar reactions from the online audience. However, the explicit signals for detecting persuasion campaigns are not clearly understood and are difficult to label. We propose an algorithm that automatically learns these signals from data, by maximizing an objective that considers their key properties. We then use the learned signals in our proposed framework for detecting persuasion campaigns in social media. In our evaluation, we find that the learned signals can improve the performance of persuasion campaign detection compared to frameworks that use signals generated by alternative methods as well as those that do not use signals.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138726/1/zhezhao_1.pd

Detecting collective attention spam

We examine the problem of collective attention spam, in which spammers target social media where user attention quickly coalesces and then collectively focuses around a phe-nomenon. Compared to many existing spam types, collec-tive attention spam relies on the users themselves to seek out the content – like breaking news, viral videos, and popular memes – where the spam will be encountered, potentially in-creasing its effectiveness and reach. We study the presence of collective attention spam in one popular service, Twitter, and we develop spam classifiers to detect spam messages generated by collective attention spammers. Since many in-stances of collective attention are bursty and unexpected, it is difficult to build spam detectors to pre-screen them before they arise; hence, we examine the effectiveness of quickly learning a classifier based on the first moments of a bursting phenomenon. Through initial experiments over a small set of trending topics on Twitter, we find encouraging results, suggesting that collective attention spam may be identified early in its life cycle and shielded from the view of unsus-pecting social media users