An Approach for Time-aware Domain-based Social Influence Prediction
Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/96242 |
| _version_ | 1848766120542076928 |
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| author | Abu Salih, Bilal Chan, Kit Yan Al-Kadi, Omar Al-Tawil, Marwan Wongthongtham, Pornpit Issa, Tomayess Saadeh, Heba Al-Hassan, Malak Bremie, Bushra Albahlal, Abdulaziz |
| author_facet | Abu Salih, Bilal Chan, Kit Yan Al-Kadi, Omar Al-Tawil, Marwan Wongthongtham, Pornpit Issa, Tomayess Saadeh, Heba Al-Hassan, Malak Bremie, Bushra Albahlal, Abdulaziz |
| author_sort | Abu Salih, Bilal |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Online Social Networks(OSNs) have established virtual platforms enabling
people to express their opinions, interests and thoughts in a variety of
contexts and domains, allowing legitimate users as well as spammers and other
untrustworthy users to publish and spread their content. Hence, the concept of
social trust has attracted the attention of information processors/data
scientists and information consumers/business firms. One of the main reasons
for acquiring the value of Social Big Data (SBD) is to provide frameworks and
methodologies using which the credibility of OSNs users can be evaluated. These
approaches should be scalable to accommodate large-scale social data. Hence,
there is a need for well comprehending of social trust to improve and expand
the analysis process and inferring the credibility of SBD. Given the exposed
environment's settings and fewer limitations related to OSNs, the medium allows
legitimate and genuine users as well as spammers and other low trustworthy
users to publish and spread their content. Hence, this paper presents an
approach incorporates semantic analysis and machine learning modules to measure
and predict users' trustworthiness in numerous domains in different time
periods. The evaluation of the conducted experiment validates the applicability
of the incorporated machine learning techniques to predict highly trustworthy
domain-based users. |
| first_indexed | 2025-11-14T11:46:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-96242 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:06Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-962422024-10-31T06:59:27Z An Approach for Time-aware Domain-based Social Influence Prediction Abu Salih, Bilal Chan, Kit Yan Al-Kadi, Omar Al-Tawil, Marwan Wongthongtham, Pornpit Issa, Tomayess Saadeh, Heba Al-Hassan, Malak Bremie, Bushra Albahlal, Abdulaziz cs.SI cs.SI cs.AI cs.IR Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users. 2020 Journal Article http://hdl.handle.net/20.500.11937/96242 unknown |
| spellingShingle | cs.SI cs.SI cs.AI cs.IR Abu Salih, Bilal Chan, Kit Yan Al-Kadi, Omar Al-Tawil, Marwan Wongthongtham, Pornpit Issa, Tomayess Saadeh, Heba Al-Hassan, Malak Bremie, Bushra Albahlal, Abdulaziz An Approach for Time-aware Domain-based Social Influence Prediction |
| title | An Approach for Time-aware Domain-based Social Influence Prediction |
| title_full | An Approach for Time-aware Domain-based Social Influence Prediction |
| title_fullStr | An Approach for Time-aware Domain-based Social Influence Prediction |
| title_full_unstemmed | An Approach for Time-aware Domain-based Social Influence Prediction |
| title_short | An Approach for Time-aware Domain-based Social Influence Prediction |
| title_sort | approach for time-aware domain-based social influence prediction |
| topic | cs.SI cs.SI cs.AI cs.IR |
| url | http://hdl.handle.net/20.500.11937/96242 |