Cross Event Detection and Topic Evolution Mining in Cross Events for Man Made Disasters in Social Media Streams
Summary: arXiv:2604.02740v1 Announce Type: cross
Abstract
Social media has transformed into a vital platform for global information sharing, especially during socially sensitive incidents such as rape, human rights marches, political controversies, and chemical attacks. These events often capture immense public attention, flooding microblogging platforms like Twitter with related tweets. As such events evolve, many similar incidents occur concurrently, which can be classified as cross events. These cross events share contextual links with the primary event and contribute to a broader discourse on the issues at hand. Understanding and disseminating information about these cross events is essential for engaging the public and fostering diverse perspectives.
The Importance of Cross Event Detection
Cross event detection plays a critical role in elucidating the nature of events. Each cross event typically has fulcrum points—specific topics around which discussions revolve. As events progress, these topics evolve, necessitating the exploration of topic evolution within the context of cross events. To address this need, we propose the Cross Event Evolution Detection (CEED) framework, which identifies cross events that exhibit temporal similarities to main events.
Methodology
The CEED framework employs a series of techniques for effective event detection:
- Tweet Segmentation: The framework utilizes the Wikipedia title database to segment tweets effectively, ensuring that the information is categorized accurately.
- Clustering Segments: Segmented tweets are clustered based on a similarity measure, which helps in identifying overlapping events both temporally and contextually.
- Cross Event Detection Algorithm: This algorithm reveals events that not only coincide in time but also share contextual themes, allowing for the evaluation of the impacts of these events on human actions.
- Topic Evolution Algorithm: This component tracks the changes in discourse topics throughout the lifecycle of an event, providing insights into how public opinion shifts over time.
Experimental Results
The efficacy of the proposed CEED framework has been validated through experiments conducted on a real Twitter dataset. The results demonstrate a high degree of precision and effectiveness in both cross event detection and topic evolution analysis. These findings underscore the framework’s potential in understanding the dynamics of cross events in social media streams, particularly in the context of man-made disasters.
Conclusion
In conclusion, the CEED framework presents a novel approach to detecting cross events and analyzing topic evolution within the realm of social media. As platforms like Twitter continue to serve as critical sources of information during crises, enhancing our understanding of cross events will be essential for informed public discourse and effective response strategies. Future work will focus on refining the algorithms and expanding the dataset to further improve the framework’s capabilities.