The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper, we propose mention-anomaly-based event detection (MABED), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e., mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users’ interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.
@cite_15: We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend. We discuss the motivation for trend detection over social media streams and the challenges that lie therein. We then describe our approach to trend detection, as well as the architecture of TwitterMonitor. Finally, we lay out our demonstration scenario.
@cite_1: Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100 of manually labeled peaks, facilitating a relatively complete view of each event studied.
@cite_2: News and events are traditionally broadcasted in an “After-the-Fact” manner, where the masses react to news formulated by a group of professionals. However, the deluge of information and real-time online social media sites have significantly changed this information input-output cycle, allowing the masses to report real-time events around the world. Specifically, the use of Twitter has resulted in the creation of a digital wealth of knowledge that directly associates to such events. Although governments and industries acknowledge the value of extracting events from the TwitterSphere, unfortunately the sheer velocity and volume of tweets poses significant challenges to the desired event analysis. In this paper, we present our Geo and Temporal Association Creator (GTAC) which extracts structured representations of events from the Twitter stream. GTAC further supports event-level investigative analysis of social media data through interactively visualizing the event indicators (who, when, where, and what). Using GTAC, we are trying to create a near real-time analysis environment for analysts to identify event structures, geographical distributions, and key indicators of emerging events.
is among the first tools developed for visualizing events from tweets. It displays a single word cloud that describes all the detected events, as well as a single stacked area chart that plots the evolution of the relative volume of tweets for each event. @cite_15 propose , a system that allows for a finer understanding of the detected events in comparison with . It displays a list of events, each event being described by a set of words and a chart that plots the evolution of the volume of related tweets. is a tool that offers similar functionalities with more sophisticated visualizations, such as word clouds to describe events instead of sets of words. @cite_1 describe , a tool whose interface revolves around a timeline of events. The user can click an event to see the tweets published during the related time interval, or to see the most cited URLs in these tweets. Let us also mention work by @cite_2 , in which a heatmap describes the distribution of events across time.