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Horn Christopher, Pimas Oliver, Granitzer Michael, Lex Elisabeth

Realtime Ad Hoc Search in Twitter: Know-Center at TREC Microblog Track 2011

Proceedings of TREC 2011, 2011

In this paper, we outline our experiments carried out at theTREC Microblog Track 2011. Our system is based on a plain text indexextracted from Tweets crawled from This index hasbeen used to retrieve candidate Tweets for the given topics. The resultingTweets were post-processed and then analyzed using three differentapproaches: (i) a burst detection approach, (ii) a hashtag analysis, and(iii) a Retweet analysis. Our experiments consisted of four runs: Firstly,a combination of the Lucene ranking with the burst detection, and secondly,a combination of the Lucene ranking, the burst detection, and thehashtag analysis. Thirdly, a combination of the Lucene ranking, the burstdetection, the hashtag analysis, and the Retweet analysis, and fourthly,again a combination of the Lucene ranking with the burst detection butin this case with more sophisticated query language and post-processing.We achieved the best MAP values overall in the fourth run.

Horn Christopher, Lex Elisabeth, Granitzer Michael

Who Tweets: Detecting User Types and Tweet Quality using Supervised Classification

IADIS Multiconference on Computer Science and Information Systems, 2011

Social networking tools like Twitter are the latest trend in the global world. However, due to the increasing amount ofcontent in Twitter, there is a need for information filtering by facets like user type and content quality. In this work, weaddress this challenge by classifying users into three user types, "news", "personal user", and "advertisements".Additionally, we assess the quality of the Tweets by classifying them into "factual" versus "opinionated". We evaluatedword stemming and regular expressions as data pre-processing techniques and found that with simple regularexpressions, a sound classification accuracy of more than 80% can be achieved. Besides, we propose a web-based TwitterClassification Application that enables to manually annotate Tweets into a set of pre-defined classes with maintainableeffort.
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