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This document describes a project called Nightcrawler that aims to detect new events in real-time from a stream of Twitter posts. It assumes that news can be defined by specific patterns in Twitter activity, like similar content among tweets, distinctive temporal and spatial distributions. The implementation uses TF-IDF to represent tweets as vectors, finds k-nearest neighbors of new tweets, and applies LSH and MaxCover algorithms to cluster tweets and detect new events, with the goal of identifying patterns before they become official news.
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DMG_final
- 1. PROJECT ‘NIGHTCRAWLER’ Real-time event mining
- 2. SOCIAL MEDIA IS RESHAPING NEWS • In recent years, social networks have become a very popular tool for expressing opinions, broadcasting news • Twitter is often the most up-to-date source of breaking news. • Roughly two-thirds (64%) of U.S. adults use social networks to obtain knowledge about latest events. • Hence, there is considerable interest in developing systems that can extract useful information from a stream of social data. • We address the problem of detecting new events from a stream of Twitter posts - real time first story detection.
- 4. NOTION OF NEWS We assume that “news” are defined by specific contextual and temporal patterns in Twitter activity. Principal component of “news” is any original content, that is going to be interesting for a significant amount of people. We propose a model that is able to detect those patterns at early stage, ideally before they become actual “news”. Those patterns often have distinctive features: • similar content among the group of tweets • specific temporal distribution • specific spatial distribution
- 5. IMPLEMENTATION SUMMARY • Extract all tweets arrived during the last 3 days from the database • Using this batch, we create a vocabulary and aTF-IDF matrix • Upon arrival, a new tweet is represented as a vector in high-dimensional (d~500k) space • We search for k-NN for this vector under a certain threshold • Found NNs become candidates for a cluster • LSH is applied with respect to NNs spatial/temporal distribution • MaxCover is used to obtain the best clusterisation • Clusters are analysed and the results are presented to the user
- 7. Instagram Photos Swarm check-ins LocationKeywords Tweets Query
- 9. ACKNOWLEDGEMENTS • Denis Antyukhov (mining, LSH, maxcover) • Fedor Chervinskii (preprocession, parsing) • Nikita Pestrov (TF-IDF, vocabulary)