Matches in ScholarlyData for { ?s ?p ?o. }
- 99 keyword "Evaluation methodologies".
- 99 keyword "Machine Translation, SpeechToSpeech Translation".
- 99 title "Performance Evaluation of Speech Translation Systems".
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- item-1 hasContent brian-weiss.
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- item-2 hasContent craig-schlenoff.
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- item-3 hasContent greg-sanders.
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- item-4 hasContent michelle-steves.
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- item-5 hasContent sherri-condon.
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- item-6 hasContent jon-phillips.
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- item-7 hasContent dan-parvaz.
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- 10 creator adrien-guille.
- 10 type InProceedings.
- 10 label "A Predictive Model for the Temporal Dynamics of Information Diffusion in Online Social Networks".
- 10 sameAs 10.
- 10 abstract "Today, online social networks have become powerful tools for the spread of information. They facilitate the rapid and large-scale propagation of content and the consequences of an inaccurate or false information can then take considerable proportions. Therefore it is essential to provide means to analyze the phenomenon of information dissemination in such networks.Many recent studies have addressed the modeling of the process of information diffusion, from a topological point of view and in a theoretical perspective, but we still know little about the factors involved in it.With the assumption that the dynamics of the spreading process at the macroscopic level is explained by interactions at microscopic level between pairs of users and the topology of their interconnections, we propose a practical solution which aims to predict the temporal dynamics of diffusion in social networks. Our approach is based on machine learning techniques and the inference of time-dependent diffusion probabilities from a multidimensional analysis of individual behaviors. Experimental results on a real dataset extracted from Twitter show the interest and effectiveness of the proposed approach as well as interesting recommendations for future investigation.".
- 10 hasAuthorList authorList.
- 10 isPartOf proceedings.
- 10 keyword "information diffusion".
- 10 keyword "machine learning".
- 10 keyword "social networks".
- 10 title "A Predictive Model for the Temporal Dynamics of Information Diffusion in Online Social Networks".
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- item-1 hasContent adrien-guille.
- 12 creator conor-hayes.
- 12 creator samantha-lam.
- 12 creator vaclav-belak.
- 12 type InProceedings.
- 12 label "Targeting Online Communities to Maximise Information Diffusion".
- 12 sameAs 12.
- 12 abstract "In recent years, many companies have started to utilise online social communities as a means of communicating with and targeting their employees and customers. Such online communities include discussion fora which are driven by the conversational activity of users. For example, users may respond to certain ideas as a result of the influence of their neighbours in the underlying social network. We analyse such influence to target communities rather than individual actors because information is usually shared with the community and not just with individual users. In this paper, we study information diffusion across communities and argue that some communities are more suitable for maximising spread than others. In order to achieve this, we develop a set of novel measures for cross-community influence, and show that it outperforms other targeting strategies on 51 weeks of data of the largest Irish online discussion system, Boards.ie.".
- 12 hasAuthorList authorList.
- 12 isPartOf proceedings.
- 12 keyword "cross-community dynamics".
- 12 keyword "discussion fora".
- 12 keyword "dynamic online communities".
- 12 keyword "information diffusion".
- 12 title "Targeting Online Communities to Maximise Information Diffusion".
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- item-1 type ListItem.
- item-1 hasContent vaclav-belak.
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- item-2 hasContent samantha-lam.
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- item-3 hasContent conor-hayes.
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- 14 creator jonny-bentwood.
- 14 creator leslie-carr.
- 14 creator ramine-tinati.
- 14 creator wendy-hall.
- 14 type InProceedings.
- 14 label "Identifying Communicator Roles in Twitter".
- 14 sameAs 14.
- 14 abstract "Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale, large user-base system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified. To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge.".
- 14 hasAuthorList authorList.
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- 14 keyword "Influence".
- 14 keyword "Twitter".
- 14 keyword "User Classification".
- 14 keyword "Web Science".