TY - GEN
T1 - Coalescing twitter trends
T2 - 2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011
AU - Brennan, Michael
AU - Greenstadt, Rachel
PY - 2011
Y1 - 2011
N2 - We demonstrate the effectiveness that machine learning can bring to improving social media platforms through a case study on Twitter trending topics. Social media relies heavily on tagging and often does not take advantage of machine learning advances. Twitter is no exception. Individual tweets are identified as being part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85% precision without using the identifying keyword as a feature. This can aid in improving the quality of topic categorization by ensuring on-topic tweets that are missing the trend keyword are included, as well as suggest keywords to include in new tweets.
AB - We demonstrate the effectiveness that machine learning can bring to improving social media platforms through a case study on Twitter trending topics. Social media relies heavily on tagging and often does not take advantage of machine learning advances. Twitter is no exception. Individual tweets are identified as being part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85% precision without using the identifying keyword as a feature. This can aid in improving the quality of topic categorization by ensuring on-topic tweets that are missing the trend keyword are included, as well as suggest keywords to include in new tweets.
UR - http://www.scopus.com/inward/record.url?scp=84856135048&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856135048&partnerID=8YFLogxK
U2 - 10.1109/PASSAT/SocialCom.2011.160
DO - 10.1109/PASSAT/SocialCom.2011.160
M3 - Conference contribution
AN - SCOPUS:84856135048
SN - 9780769545783
T3 - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
SP - 641
EP - 646
BT - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
Y2 - 9 October 2011 through 11 October 2011
ER -