Discovering hidden topical hubs and authorities across multiple online social networks

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In r...

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Main Authors: LEE, Ka Wei, Roy, HOANG, Tuan-Anh, LIM, Ee-Peng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Hub
Online Access:https://ink.library.smu.edu.sg/sis_research/6046
https://ink.library.smu.edu.sg/context/sis_research/article/7055/viewcontent/08736791.pdf
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spelling sg-smu-ink.sis_research-70552021-08-03T10:14:57Z Discovering hidden topical hubs and authorities across multiple online social networks LEE, Ka Wei, Roy HOANG, Tuan-Anh LIM, Ee-Peng Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users’ relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHATmodel, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs. 2021-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6046 info:doi/10.1109/TKDE.2019.2922962 https://ink.library.smu.edu.sg/context/sis_research/article/7055/viewcontent/08736791.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Hub authority topic model online social networks Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Hub
authority
topic model
online social networks
Numerical Analysis and Scientific Computing
spellingShingle Hub
authority
topic model
online social networks
Numerical Analysis and Scientific Computing
LEE, Ka Wei, Roy
HOANG, Tuan-Anh
LIM, Ee-Peng
Discovering hidden topical hubs and authorities across multiple online social networks
description Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users’ relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHATmodel, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.
format text
author LEE, Ka Wei, Roy
HOANG, Tuan-Anh
LIM, Ee-Peng
author_facet LEE, Ka Wei, Roy
HOANG, Tuan-Anh
LIM, Ee-Peng
author_sort LEE, Ka Wei, Roy
title Discovering hidden topical hubs and authorities across multiple online social networks
title_short Discovering hidden topical hubs and authorities across multiple online social networks
title_full Discovering hidden topical hubs and authorities across multiple online social networks
title_fullStr Discovering hidden topical hubs and authorities across multiple online social networks
title_full_unstemmed Discovering hidden topical hubs and authorities across multiple online social networks
title_sort discovering hidden topical hubs and authorities across multiple online social networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6046
https://ink.library.smu.edu.sg/context/sis_research/article/7055/viewcontent/08736791.pdf
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