Local experts finding using user comments in location‐based social networks

Cao, Jiuxin; Yang, Yuntao; Cao, Biwei; Xue, Lei; Li, Shancang; Iqbal, Muddesar; Mumtaz, Shahid

doi:10.1002/ett.3600

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…In addition, using the list of influential friends based on their ranks, the service provider can utilize it for the contact tracing purpose when the mobile user or one of his/her influential friends in the list gets infected. Moreover, the local-experts-finding scheme proposed in [151] can be utilized to find the local social media users of a certain area. Based on this, information such as current crowds locations can be extracted more efficiently.…”

Section: Emerging Technologies For Social Distancingmentioning

confidence: 99%

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

et al. 2020

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This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I [1], an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.

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Section: Emerging Technologies For Social Distancingmentioning

confidence: 99%

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

et al. 2020

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“…Furthermore, because unverified information is spread by non-experts, users must determine the level of trustworthiness of the information circulating on social media. In such an environment, social media users wish to find experts who can provide the desired information or answers to their queries [14][15][16]. Recently, services have been developed to answer questions by social media users [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Quora, for example, is an online social service that enables anyone to ask questions and comment on the answers that have been submitted by other users. On social media, users can get answers to questions through 2 of 16 Q&A services by registering feature problems and questions online. One problem with these Q&A services is that there may be inaccurate answers because many unspecified users can answer questions, and there may be no answers to specific questions.…”

Section: Introductionmentioning

confidence: 99%

Expert Recommendation for Answering Questions on Social Media

et al. 2021

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In this paper, we propose a method for recommending experts to appropriately answer questions based on social activity analysis on social media. By analyzing various social activities performed on social media, the user’s interests are identified. Through the human relation analysis of the users of a particular interest field and by considering the response speed and answer quality of the user, we determine the influence of a user. An expert group is matched by analyzing the content of queries by a user and using a hierarchical structure of words. For a user question, the accuracy of an expert recommendation is enhanced by incorporating the question content and sublevel words based on the hierarchical structure of words. Various evaluations have demonstrated that the performance of the proposed method is superior to existing methods.

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“…In this cyber era, vast number of peoples involved in social networks such as Facebook, twitter, Flickr, and so on, to share their thoughts and information. [1][2][3] Involvement of numerous users and dynamic nature of social networks open the door for social network analysis (SNA). [4][5][6][7][8] SNA attempts to understand the structure of dynamic social network and its participants.…”

Section: Introductionmentioning

confidence: 99%

Hybrid machine learning approach for community and overlapping community detection in social network

Divyapushpalakshmi

Ramalakshmi

2020

Trans Emerging Tel Tech

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Social Network Analysis (SNA) is one of the significant fields of sociology, which probes many researchers toward it. In SNA, overall social network (such as Facebook, Twitter, and so on) structure is learned by community detection process.However, dynamic nature and involvement of numerous users in social network limits the accuracy of community detection process. In this research work, we propose a novel dual examining and hybrid machine learning approaches for community and overlapping community detection. We collect recent tweets from twitter social network in real time as training dataset. An undirected graph is constructed over collected twitter dataset to enhance the community detection process. Upon constructed graph, informative examining and structural examining processes are applied. Informative examining results with informative factor whereas structural examining identifies influencing node. Structural examining is performed using Particle Swarm Optimization algorithm based on centrality factor and node strength factor. By utilizing results from dual examining processes, community detection is performed by a hybrid machine learning approach Naïve Bayes with Firefly Algorithm. Overlapping community detection is followed up to the community detection using Fuzzy with neural network classifier. Here, Jaccard Similarity factor, overlapping coefficient, and modularity are considered in fuzzy with neural network. Extensive simulation results show better performance than single examining methods in terms of accuracy of mutual information, ratio of community, run time, and average degree in proposed hybrid machine learning approach-based community detection.

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Local experts finding using user comments in location‐based social networks

Cited by 8 publications

References 25 publications

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

Expert Recommendation for Answering Questions on Social Media

Hybrid machine learning approach for community and overlapping community detection in social network

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