With the increasing use of online communication platforms, such as email, twitter, and messaging applications, we are faced with a growing amount of data that combine content (what is said), time (when), and user (by whom) information. An important computational challenge is to analyze these data, discover meaningful patterns, and understand what is happening. We consider the problem of mining online communication data and finding top-k temporal events. We define a temporal event to be a coherent topic that is discussed frequently, in a relatively short time span, while the information ow of the event respects the underlying network structure. We construct our model for detecting temporal events in two steps. We first introduce the notion of interaction meta-graph, which connects associated interactions. Using this notion, we define a temporal event to be a subset of interactions that (i) are topically and temporally close and (ii) correspond to a tree that captures the information ow. Finding the best temporal event leads to budget version of the prize-collecting Steiner-tree (PCST) problem, which we solve using three different methods: a greedy approach, a dynamic-programming algorithm, and an adaptation to an existing approximation algorithm. The problem of finding the top- k events among a set of candidate events maps to maximum set-cover problem, and thus, solved by greedy. We compare and analyze our algorithms in both synthetic and real datasets, such as twitter and email communication. The results show that our methods are able to detect meaningful temporal events.
Phrase-based event detection. The problem of detecting events in social media has attracted significant attention. @cite_19 and @cite_20 treat events as short, distinctive phrases that propagate relatively intact through in a network. Their work offers a graph formulation for clustering variants of phrases based on string edit distance. Although their objective is similar to ours, there are significant differences. First, our methods focus on interaction networks, aiming to capture information flows in communication networks, rather than action networks. Second, we explicitly impose topic-coherence constraints, where the edit distance is insufficient for this goal. Third, instead of representing events by phrases, we derive higher-level representation using topic terms.
@cite_19 @cite_20
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Event detection in social media is an important but challenging problem. Most existing approaches are based on burst detection, topic modeling, or clustering techniques, which cannot naturally model the implicit heterogeneous network structure in social media. As a result, only limited information, such as terms and geographic locations, can be used. This paper presents Non-Parametric Heterogeneous Graph Scan (NPHGS), a new approach that considers the entire heterogeneous network for event detection: we first model the network as a "sensor" network, in which each node senses its "neighborhood environment" and reports an empirical p-value measuring its current level of anomalousness for each time interval (e.g., hour or day). Then, we efficiently maximize a nonparametric scan statistic over connected subgraphs to identify the most anomalous network clusters. Finally, the event represented by each cluster is summarized with information such as type of event, geographical locations, time, and participants. As a case study, we consider two applications using Twitter data, civil unrest event detection and rare disease outbreak detection, and present empirical evaluations illustrating the effectiveness and efficiency of our proposed approach. With the fast growth of smart devices and social networks, a lot of computing systems collect data that record different types of activities. An important computational challenge is to analyze these data, extract patterns, and understand activity trends. We consider the problem of mining activity networks to identify interesting events, such as a big concert or a demonstration in a city, or a trending keyword in a user community in a social network. We define an event to be a subset of nodes in the network that are close to each other and have high activity levels. We formalize the problem of event detection using two graph-theoretic formulations. The first one captures the compactness of an event using the sum of distances among all pairs of the event nodes. We show that this formulation can be mapped to the maxcut problem, and thus, it can be solved by applying standard semidefinite programming techniques. The second formulation captures compactness using a minimum-distance tree. This formulation leads to the prize-collecting Steiner-tree problem, which we solve by adapting existing approximation algorithms. For the two problems we introduce, we also propose efficient and effective greedy approaches and we prove performance guarantees for one of them. We experiment with the proposed algorithms on real datasets from a public bicycling system and a geolocation-enabled social network dataset collected from twitter. The results show that our methods are able to detect meaningful events. This paper investigates the use and extension of text retrieval and clustering techniques for event detection. The task is to automatically detect novel events from a temporally-ordered stream of news stories, either retrospectively or as the stories arrive. We applied hierarchical and non-hierarchical document clustering algorithms to a corpus of 15,836 stories, focusing on the exploitation of both content and temporal information. We found the resulting cluster hierarchies highly informative for retrospective detection of previously unidentified events, effectively supporting both query-free and query-driven retrieval. We also found that temporal distribution patterns of document clusters provide useful information for improvement in both retrospective detection and on-line detection of novel events. In an evaluation using manually labelled events to judge the system-detected events, we obtained a result of 82 in the Fl measure for retrospective detection, and a Fl value of 42 for on-line detection. In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in videos. The proposed approach has three main steps. First, in order to learn the event structure from training videos, we automatically encode the sub-event dependency graph, which is the learnt event model that depicts the conditional dependency between sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events using normalized cuts. The principal assumption made in this work is that the events are composed of a highly correlated chain of sub-events that have high weights (association) within the cluster and relatively low weights (disassociation) between the clusters. The event detection does not require prior knowledge of the number of agents involved in an event and does not make any assumptions about the length of an event. Third, we recognize the fact that any abstract event model should extend to representations related to human understanding of events. Therefore, we propose an extension of CASE representation of natural languages that allows a plausible means of interface between users and the computer. We show results of learning, detection, and representation of events for videos in the meeting, surveillance, and railroad monitoring domains.