Mining Concurrent Topical Activity in Microblog Streams

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11, 2011

In recent years social media have become indispensable tools for information dissemination, operating in tandem with traditional media outlets such as newspapers, and it has become critical to understand the interaction between the new and old sources of news. Although social media as well as traditional media have attracted attention from several research communities, most of the prior work has been limited to a single medium. In addition temporal analysis of these sources can provide an understanding of how information spreads and evolves. Modeling temporal dynamics while considering multiple sources is a challenging research problem. In this paper we address the problem of modeling text streams from two news sources -Twitter and Yahoo! News. Our analysis addresses both their individual properties (including temporal dynamics) and their inter-relationships. This work extends standard topic models by allowing each text stream to have both local topics and shared topics. For temporal modeling we associate each topic with a time-dependent function that characterizes its popularity over time. By integrating the two models, we effectively model the temporal dynamics of multiple correlated text streams in a unified framework. We evaluate our model on a large-scale dataset, consisting of text streams from both Twitter and news feeds from Yahoo! News. Besides overcoming the limitations of existing models, we show that our work achieves better perplexity on unseen data and identifies more coherent topics. We also provide analysis of finding real-world events from the topics obtained by our model.

2013

Social media platforms such as Twitter and Facebook have seen increasing adoption by people worldwide. Coupled with the habit of people to use social media for sharing their daily activities and experiences, it is not surprising that a substantial part of real-world events are well described by the online streams of status updates, posts and media content. In fact, in the case of large events, such as festivals, the number of online messages and shared content can be so high that it is very hard to get an objective view of the event. To this end, this paper presents EventSense, a social media sensing framework that can help event organizers and enthusiasts capture the pulse of large events and gain valuable insights into their impact on visitors. More specifically, EventSense enables the automatic association of online messages to entities of interest (e.g. films in the case of a film festival), the automatic discovery of topics discussed online, and the detection of sentiment (positive/negative/neutral) both at an entity level (e.g. per film) and on aggregate. In addition, the framework produces an informative social media summary of the event of interest by automatically selecting and putting together its highlights, e.g. the most discussed entities and topics, the most influential users, the evolution of the discussions' sentiment, and the most shared media and news content. A real-world case study is presented by applying EventSense on a rich dataset collected around the 53rd Thessaloniki International Film Festival.

IEEE Access, 2017

Social media plays an increasingly important role in people's life. Microblogging is a form of social media which allows people to share and disseminate real-life events. Broadcasting events in microblogging networks can be an effective method of creating awareness, divulging important information, and so on. However, many existing approaches at dissecting the information content primarily discuss the event detection model and ignore the user interest which can be discovered during event evolution. This leads to difficulty in tracking the most important events as they evolve including identifying the influential spreaders. There is further complication given that the influential spreaders interests will also change during event evolution. The influential spreaders play a key role in event evolution and this has been largely ignored in traditional event detection methods. To this end, we propose a user-interest model-based event evolution model, named the hot event evolution model. This model not only considers the user interest distribution but also uses the short text data in the social network to model the posts and the recommend methods to discover the user interests. This can resolve the problem of data sparsity, as exemplified by many existing event detection methods, and improve the accuracy of event detection. A hot event automatic filtering algorithm is initially applied to remove the influence of general events, improving the quality and efficiency of mining the event. Then, an automatic topic clustering algorithm is applied to arrange the short texts into clusters with similar topics. An improved user-interest model is proposed to combine the short texts of each cluster into a long text document simplifying the determination of the overall topic in relation to the interest distribution of each user during the evolution of important events. Finally, a novel cosine measurebased event similarity detection method is used to assess correlation between events, thereby detecting the process of event evolution. The experimental results on a real Twitter data set demonstrate the efficiency and accuracy of our proposed model for both event detection and user interest discovery during the evolution of hot events. INDEX TERMS Microblogging, event evolution, user topic.

International Journal of Web Information Systems, 2018

Purpose The purpose of this paper is to propose a framework for intelligent analysis of Twitter data. The purpose of the framework is to allow users to explore a collection of tweets by extracting topics with semantic relevance. In this way, it is possible to detect groups of tweets related to new technologies, events and other topics that are automatically discovered. Design/methodology/approach The framework is based on a three-stage process. The first stage is devoted to dataset creation by transforming a collection of tweets in a dataset according to the vector space model. The second stage, which is the core of the framework, is centered on the use of non-negative matrix factorizations (NMF) for extracting human-interpretable topics from tweets that are eventually clustered. The number of topics can be user-defined or can be discovered automatically by applying subtractive clustering as a preliminary step before factorization. Cluster analysis and word-cloud visualization are u...

PLoS ONE, 2014

Twitter is a major social media platform in which users send and read messages (''tweets'') of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible ''thermostats'' of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

2012

Abstract During broadcast events such as the Superbowl, the US Presidential and Primary debates, etc., Twitter has become the de facto platform for crowds to share perspectives and commentaries about them. Given an event and an associated large-scale collection of tweets, there are two fundamental research problems that have been receiving increasing attention in recent years. One is to extract the topics covered by the event and the tweets; the other is to segment the event.

Twitter has brought a paradigm shift in the way we produce and curate information about real-life events. Huge volumes of user-generated tweets are produced in Twitter, related to events. Not, all of them are useful and informative. A sizable amount of tweets are spams and colloquial personal status updates, which does not provide any useful information about an event. Thus, it is necessary to identify, rank and segregate event-specific informative content from the tweet streams. In this paper, we develop a novel generic framework based on the principle of mutual reinforcement, for identifying event-specific informative content from Twitter. Mutually reinforcing relationships between tweets, hashtags, text units, URLs and users are defined and represented using TwitterEventInfoGraph. An algorithm - TwitterEventInfoRank is proposed, that simultaneously ranks tweets, hashtags, text units, URLs and users producing them in terms of event-specific informativeness by leveraging the semantics of relationships between each of them as represented by TwitterEventInfoGraph. Experiments and observations are reported on four million (approx) tweets collected for five real-life events, and evaluated against popular baseline techniques showing significant improvement in performance.

ArXiv, 2021

The detection of events from online social networks is a recent, evolving field that attracts researchers from across a spectrum of disciplines and domains. Here we report a time-series analysis for predicting events. In particular, we evaluated the frequency distribution of top n-grams of terms over time, focusing on two indicators: high-frequency n-grams over both short and long periods of time. Both indicators can refer to certain aspects of events as they evolve. To evaluate the model’s accuracy in detecting events, we built and used a Twitter dataset of the mostpopular hashtags that surrounded the well-documented protests that occurred at the University of Missouri (Mizzou) in late 2015.

arXiv (Cornell University), 2016

The tremendous growth of social media content on the Internet has inspired the development of the text analytics to understand and solve real-life problems. Leveraging statistical topic modelling helps researchers and practitioners in better comprehension of textual content as well as provides useful information for further analysis. Statistical topic modelling becomes especially important when we work with large volumes of dynamic text, e.g., Facebook or Twitter datasets. In this study, we summarize the message content of four data sets of Twitter messages relating to challenging social events in Kenya. We use Latent Dirichlet Allocation (LDA) topic modelling to analyse the content. Our study uses two evaluation measures; Normalized Mutual Information (NMI) and topic coherence analysis, to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.

ACM Transactions on Management Information Systems, 2013

Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both realworld events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query "what are people talking about?" Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a highvelocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find "hot" stories from high-rate Twitterscale text streams.