際際滷shows by User: sultanalzahrani1 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: sultanalzahrani1 / Fri, 03 Apr 2015 09:00:21 GMT 際際滷Share feed for 際際滷shows by User: sultanalzahrani1 A network based model for predicting a hashtag break out in twitter /slideshow/a-network-based-model-for-predicting-a-hashtag-break-out-in-twitter/46613560 anetworkbasedmodelforpredictingahashtagbreakoutintwitter-150403090021-conversion-gate01
Online information propagates differently on the web, some of which can be viral. In this paper, first we introduce a simple standard deviation sigma levels based Tweet volume breakout definition, then we proceed to determine patterns of re-tweet network measures to predict whether a hashtag volume will breakout or not. We also developed a visualization tool to help trace the evolution of hashtag volumes, their underlying networks and both local and global network measures. We trained a random forest tree classifier to identify effective network measures for predicting hashtag volume breakouts. Our experiments showed that local network features, based on a fixed-sized sliding window, have an overall predictive accuracy of 76 %, where as, when we incorporate global features that utilize all interactions up to the current period, then the overall predictive accuracy of a sliding window based breakout predictor jumps to 83 %.]]>
Online information propagates differently on the web, some of which can be viral. In this paper, first we introduce a simple standard deviation sigma levels based Tweet volume breakout definition, then we proceed to determine patterns of re-tweet network measures to predict whether a hashtag volume will breakout or not. We also developed a visualization tool to help trace the evolution of hashtag volumes, their underlying networks and both local and global network measures. We trained a random forest tree classifier to identify effective network measures for predicting hashtag volume breakouts. Our experiments showed that local network features, based on a fixed-sized sliding window, have an overall predictive accuracy of 76 %, where as, when we incorporate global features that utilize all interactions up to the current period, then the overall predictive accuracy of a sliding window based breakout predictor jumps to 83 %.]]> Fri, 03 Apr 2015 09:00:21 GMT /slideshow/a-network-based-model-for-predicting-a-hashtag-break-out-in-twitter/46613560 sultanalzahrani1@slideshare.net(sultanalzahrani1) A network based model for predicting a hashtag break out in twitter sultanalzahrani1 Online information propagates differently on the web, some of which can be viral. In this paper, first we introduce a simple standard deviation sigma levels based Tweet volume breakout definition, then we proceed to determine patterns of re-tweet network measures to predict whether a hashtag volume will breakout or not. We also developed a visualization tool to help trace the evolution of hashtag volumes, their underlying networks and both local and global network measures. We trained a random forest tree classifier to identify effective network measures for predicting hashtag volume breakouts. Our experiments showed that local network features, based on a fixed-sized sliding window, have an overall predictive accuracy of 76 %, where as, when we incorporate global features that utilize all interactions up to the current period, then the overall predictive accuracy of a sliding window based breakout predictor jumps to 83 %. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/anetworkbasedmodelforpredictingahashtagbreakoutintwitter-150403090021-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Online information propagates differently on the web, some of which can be viral. In this paper, first we introduce a simple standard deviation sigma levels based Tweet volume breakout definition, then we proceed to determine patterns of re-tweet network measures to predict whether a hashtag volume will breakout or not. We also developed a visualization tool to help trace the evolution of hashtag volumes, their underlying networks and both local and global network measures. We trained a random forest tree classifier to identify effective network measures for predicting hashtag volume breakouts. Our experiments showed that local network features, based on a fixed-sized sliding window, have an overall predictive accuracy of 76 %, where as, when we incorporate global features that utilize all interactions up to the current period, then the overall predictive accuracy of a sliding window based breakout predictor jumps to 83 %.
A network based model for predicting a hashtag break out in twitter from Sultan Alzahrani
]]> 551 3 https://cdn.slidesharecdn.com/ss_thumbnails/anetworkbasedmodelforpredictingahashtagbreakoutintwitter-150403090021-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png