On the Predictive Power of Web Intelligence and Social Media

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• 关键词：Web intelligence ; Open ; source intelligence ; Web and social media mining ; Twitter analysis ; Forecasting ; Event extraction ; Temporal analytics ; Sentiment analysis
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9546
• 期：1
• 页码：26-45
• 全文大小：900 KB
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• 作者单位：Nathan Kallus (18)
  
  18. Massachusetts Institute of Technology, 77 Massachusetts Ave E40-149, Cambridge, MA, 02139, USA
  
• 丛书名：Big Data Analytics in the Social and Ubiquitous Context
• ISBN：978-3-319-29009-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

With more information becoming widely accessible and new content created every day on today’s web, more are turning to harvesting such data and analyzing it to extract insights. But the relevance of such data to see beyond the present is not clear. We present efforts to predict future events based on web intelligence – data harvested from the web – with specific emphasis on social media data and on timed event mentions, thereby quantifying the predictive power of such data. We focus on predicting crowd actions such as large protests and coordinated acts of cyber activism – predicting their occurrence, specific timeframe, and location. Using natural language processing, statements about events are extracted from content collected from hundred of thousands of open content web sources. Attributes extracted include event type, entities involved and their role, sentiment and tone, and – most crucially – the reported timeframe for the occurrence of the event discussed – whether it be in the past, present, or future. Tweets (Twitter posts) that mention an event to occur reportedly in the future prove to be important predictors. These signals are enhanced by cross referencing with the fragility of the situation as inferred from more traditional media, allowing us to sift out the social media trends that fizzle out before materializing as crowds on the ground.