Learning similarity metrics for event identification in social media

Abstract

Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events of different type and scale. By automatically identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can enable event browsing and search in state-of-the-art search engines. To address this problem, we exploit the rich "context" associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). Using this rich context, which includes both textual and non-textual features, we can define appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we explore a variety of techniques for learning multi-feature similarity metrics for social media documents in a principled manner. We evaluate our techniques on large-scale, realworld datasets of event images from Flickr. Our evaluation results suggest that our approach identifies events, and their associated social media documents, more effectively than the state-of-the-art strategies on which we build.

Temporal Analysis of Social Media Response to Live Events: The Milano Fashion Week

“…The work [10] is concerned with 21 hot events which were widely discussed on Sina Weibo; it empirically analyzes their posting and reposting characteristics. In the work [2], by automatically identifying events and their associated user-contributed social media documents, the authors show how they can enable event browsing and search in a search engine. The work [3] underlines how user-contributed messages on social media sites such as Twitter have emerged as powerful, real-time means of information sharing on the Web.…”

Arabic Event Detection in Social Media

“…Becker et al [21] proposed an online clustering framework, suitable for large-scale social media sites such as Twitter, to identify different types of real-world events. The online clustering technique groups together topically similar tweets and implements four features (Temporal features, Social features, Topical Features and TwitterCentric Features) to distinguish between real-world events and non-events.…”

“…As we do not know the number of events and sub-events a priori the online clustering is suitable as it does not require such input; (iii) partitioning algorithms are ineffective in this case because of the high and constant sheer scale of tweets [21].…”

“…Summarization task is a very challenging task in its own and takes various forms [23]. The simplest approach is to consider each tweet as a document, and then apply a summarization method on this corpus to capture its key features [17,21,23]. Voting algorithms [17] are utilized in applications where in the context of microblogging sites take into account the following:…”

“…The volume of tweets, and the continually updated commentary around an event suggests that informative tweets from several hours ago may not be as important as new tweets [21]. For this reason we retain the most frequently occurring terms a cluster in hourly time frames and compare the number of tweets posted during an hour that contain term t to the total number of tweets posted during that hour.…”