Cross Event Detection and Topic Evolution Mining in cross events for Man Made Disasters in Social Media Streams

• URL: http://arxiv.org/abs/2604.02740v1
• Date: Fri, 03 Apr 2026 05:21:47 GMT
• Title: Cross Event Detection and Topic Evolution Mining in cross events for Man Made Disasters in Social Media Streams
• Abstract summary: Social media is widely used to share information globally and it also aids to gain attention from the world.<n>Cross event detection is critical in determining the nature of events.<n>We have proposed Cross Event Evolution Detection CEED framework which detects cross events that are similar with regards to their temporal nature resulting from main events.
• Score: 1.5049442691806052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media is widely used to share information globally and it also aids to gain attention from the world. When socially sensitive incidents like rape, human rights march, corruption, political controversy, chemical attacks occur, they gain immense attention from people all over the world, causing microblogging platforms like Twitter to get flooded with tweets related to such events. When an event evolves, many other events of a similar nature have happened in and around the same time frame. These are cross events because they are linked to the nature of the main event. Dissemination of information relating to such cross events helps in engaging the masses to share the varied views that emerge out of the similarities and differences between the events. Cross event detection is critical in determining the nature of events. Cross events have fulcrums points, i.e., topics around which the discussion is focused, as the event evolves which must be considered in topic evolution. We have proposed Cross Event Evolution Detection CEED framework which detects cross events that are similar with regards to their temporal nature resulting from main events. Event detection is based on the tweet segmentation using the Wikipedia title database and clustering segments based on a similarity measure. The cross event detection algorithm reveals events that overlap in both time and context to evaluate the effects of these cross events on deliberate negligent human actions. The topic evolution algorithm puts into perspective the change in topics for an events lifetime. The experimental results on a real Twitter data set demonstrate the effectiveness and precision of our proposed framework for both cross event detection and topic evolution algorithm during the evolution of cross events.

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