Event detection using Twitter and structured semantic query expansion

Abstract

Twitter is a popular tool for publishing potentially interesting information about people's opinions, experiences and news. Mobile devices allow people to publish tweets during real-time events. It is often difficult to identify the subject of a tweet because Twitter users often write using highly unstructured language with many typographical errors. Structured data related to entities can provide additional context to tweets. We propose an approach which associates tweets to a given event using query expansion and relationships defined on the Semantic Web, thus increasing the recall whilst maintaining or improving the precision of event detection. In this work, we investigate the usage of Twitter in discussing the Rock am Ring music festival. We aim to use prior knowledge of the festival's lineup to associate tweets with the bands playing at the festival. In order to evaluate the effectiveness of our approach, we compare the lifetime of the Twitter buzz surrounding an event to the actual programmed event, using Twitter users as social sensors.

Cluster-discovery of Twitter messages for event detection and trending

“…Becker et al uses online clustering technique to group topically similar tweets and their system was able to classify them under real-world or non-real world events [29]. Packer et al proposed an approach which associates tweets to a given event using query expansion [9].…”

“…The text in social media is often noisy and the high volume of data presents challenges to the researchers attempting to find a technique suitable for event detection in social media. Today some solutions, including topic detection and tracking (TDT) [2,7,8] over newswire system and event detection in social media [9][10][11] have been employed in the data mining related researches. However, there is a great potential in research for extracting events from Twitter.…”

Semantic query suggestion using Twitter Entities

“…Even in our own previous work [2], we proposed the utilization of hashtags as the main source of information acquisition, by searching the specific query terms within microblog posts under the condition that the former need to appear as hashtags; then, we calculated the most common hashtags that co-occur together with the original query, and, thus, expanded the query with the new hashtags. Last but not least, the observation that microblog posts are created during an actual event and contain comments and/or information directly related to it leads to event detection research efforts [18] based on posts and/or hashtags.…”

Toward optimal allocation of location dependent tasks in crowdsensing

“…This, without the involvement of the crowd, would be costly and even be impossible to achieve. Other similar systems have been proposed to make crowdsensing possible for other environmental, infrastructural and social applications [7]- [12].…”