Hits 1.141 – 1.159 of 1.159
1141 |
When social bots attack: Modeling susceptibility of users in online social networks
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In: http://www.kmi.tugraz.at/staff/markus/documents/2012_MSM12_socialbots.pdf
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1142 |
Major Life Changes and Behavioral Markers in Social Media: Case of Childbirth
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In: http://research.microsoft.com/%7Ehorvitz/cscw_2013_childbirth.pdf
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1143 |
Classifying Short Text in Social Media: Twitter as Case Study
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In: http://research.ijcaonline.org/volume111/number9/pxc3901321.pdf
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1144 |
Named Entity Extraction and Linking Challenge: University of Twente at #Microposts2014
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In: http://ceur-ws.org/Vol-1141/paper_13.pdf
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1145 |
Annotating Irony in a Novel Italian Corpus for Sentiment Analysis
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In: http://www.di.unito.it/~argo/papers/2012_ES312-sentiTut.pdf
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1146 |
The Effects of Feedback on Human Behavior in Social Media: An Inverse Reinforcement Learning Model
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In: http://www.cse.wustl.edu/%7Eallenlavoie/papers/reddit.pdf
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1147 |
Votter Corpus: A Corpus of Social Polling Language
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In: http://www.lrec-conf.org/proceedings/lrec2014/pdf/1143_Paper.pdf
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1148 |
Negative Link Prediction in Social Media
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In: http://www.public.asu.edu/%7Ejtang20/publication/negative_link_prediction.pdf
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Abstract:
Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed net-work analysis suggests that negative links have added value in the analytical process. A major impediment in their effec-tive use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists be-tween the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the com-monly available social network data. In this paper, we in-vestigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interac-tions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.
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Keyword:
Networks; Social Media
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URL: http://www.public.asu.edu/%7Ejtang20/publication/negative_link_prediction.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.680.8707
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1149 |
CLIR for Informal Content in Arabic Forum Posts
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In: http://terpconnect.umd.edu/%7Eoard/pdf/cikm14-bagdouri.pdf
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1150 |
Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach.
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1151 |
European comission's identity during brexit: a communication strategy change on twitter
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1152 |
MULTISENSOR: development of multimedia content integration technologies for journalism, media monitoring and international exporting decision support
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1153 |
Detecting signs of depression in tweets in spanish: behavioral and linguistic analysis
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1156 |
Evaluating behavioral and linguistic changes during drug treatment for depression using tweets in spanish: pairwise comparison study
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1157 |
UPF’s participation at the CLEF eRisk 2018: early risk prediction on the Internet
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1159 |
The influence of social media in language change: Changes in vocabulary
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