Mining expertise and interests from social media

Guy, Ido; Avraham, Uri; Carmel, David; Ur, Shmuel; Jacovi, Michal; Ronen, Inbal

doi:10.1145/2488388.2488434

Cited by 81 publications

(36 citation statements)

References 38 publications

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“…Similar to most crowdsourcing systems, we can ask workers to provide their profiles when they register in our system. Furthermore, inspired by the studies on expert finding from social media [37,38], we can also learn human expertise or experience from user-generated data in social media. For example, user check-ins in location-based social networks allows us to learn the familiarity of a user to the task venue; online posts of a user can expose her interests or expertise.…”

Section: Discussionmentioning

confidence: 99%

ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

Guo

Liu

et al. 2017

IEEE Trans. Human-Mach. Syst.

220

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Abstract-Worker selection is a key issue in Mobile Crowd Sensing (MCS). While previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, multi-task-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multi-task MCS environments. We study the problem of multi-task worker selection under two situations: worker selection based on workers' intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two Greedy-enhanced Genetic Algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).

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Section: Discussionmentioning

confidence: 99%

ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

Guo

Liu

et al. 2017

IEEE Trans. Human-Mach. Syst.

220

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“…Guy et al propose as a further research question to investigate how the use of social media contributes to an increasing level of expertise within organisations [39].…”

Section: Effects Of Esn Usagementioning

confidence: 99%

Enterprise Social Networks: Status Quo of Current Research and Future Research Directions

Stei

Sprenger

Rossmann

2016

Business Information Systems

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Abstract. This paper provides an introduction to the topic of Enterprise Social Networks (ESN) and illustrates possible applications, potentials, and challenges for future research. It outlines an analysis of research papers containing a literature overview in the field of ESN. Subsequently, single relevant research papers are analysed and further research potentials derived therefrom. This yields seven promising areas for further research: (1) user behaviour; (2) effects of ESN usage; (3) management, leadership, and governance; (4) value assessment and success measurement; (5) cultural effects, (6) architecture and design of ESN; and (7) theories, research designs and methods. This paper characterises these areas and articulates further research directions.

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“…Another related topic is to mine expertise from social media [12]. Guy et al [12] made the first analysis of various information signals from social media for expertise and interest mining in an enterprise.…”

Section: Expertise Miningmentioning

confidence: 99%

“…Guy et al [12] made the first analysis of various information signals from social media for expertise and interest mining in an enterprise. Yang et al [34] propose to joint model topics and expertise in community question answering systems.…”

Section: Expertise Miningmentioning

confidence: 99%

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Zhao

Liu

et al. 2015

World Wide Web

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Social media influence analysis, sometimes also called authority detection, aims to rank users based on their influence scores in social media. Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users' influence scores. They rarely consider a person's expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally "Sina microblogging"). We found that there is a strong correlation between expertise levels and social media influence scores. Our analysis gave a good explanation of the phenomenon of "top across-domain influencers". In addition, different expertise levels showed influence variation patterns: e.g., (1) high-expertise celebrities have stronger influence on World Wide Web the "audience" in their expertise domains; (2) expertise seems to be more important than relevance and participation for social media influence; (3) the audiences of top expertise celebrities are more likely to forward tweets on topics outside the expertise domains from high-expertise celebrities.

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Mining expertise and interests from social media

Cited by 81 publications

References 38 publications

ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

Enterprise Social Networks: Status Quo of Current Research and Future Research Directions

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

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