A New Framework for Discovering Important Posts and Influential Users in Social Networks
The popularity of social networks has rapidly increased over the past few years. Social networks provide many kinds of services and benefits to their users like helping them to communicate, click, view and share contents that reflect their opinions or interests. Detecting important contents defined...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Iran Telecom Research Center
2019-12-01
|
| Series: | International Journal of Information and Communication Technology Research |
| Subjects: | |
| Online Access: | http://ijict.itrc.ac.ir/article-1-448-en.html |
| _version_ | 1811169247129239552 |
|---|---|
| author | Leila Rabiei Mojtaba Mazoochi Farzaneh Rahmani |
| author_facet | Leila Rabiei Mojtaba Mazoochi Farzaneh Rahmani |
| author_sort | Leila Rabiei |
| collection | DOAJ |
| description | The popularity of social networks has rapidly increased over the past few years. Social networks provide many kinds of services and benefits to their users like helping them to communicate, click, view and share contents that reflect their opinions or interests. Detecting important contents defined as the most visited posts and users whom disseminate them can provide some interesting insights from cyberspace user’s activities. In this paper, a framework for discovering important posts (most popular posts by views count) and influential users is introduced. The proposed framework employed on Telegram instant messaging service in this study but it is also applicable to other social networks such as Instagram and Twitter. This framework continuously works in a real social network analysis system named Zekavat to find daily important posts and influential users. The effectiveness of this framework was shown in experiments. The accuracy achieved in the advertisement detection model is 89%. Text-based clustering part of the framework was tested based on the human factor verification and clustering time is less than linear. Graph creation based on publishing relationships is more effective than mention relationship and in this process influential users can be identified in a precise manner. |
| first_indexed | 2024-04-10T16:40:17Z |
| format | Article |
| id | doaj.art-a8be17b22f5f4eb4af861a4a19657bb1 |
| institution | Directory Open Access Journal |
| issn | 2251-6107 2783-4425 |
| language | English |
| last_indexed | 2024-04-10T16:40:17Z |
| publishDate | 2019-12-01 |
| publisher | Iran Telecom Research Center |
| record_format | Article |
| series | International Journal of Information and Communication Technology Research |
| spelling | doaj.art-a8be17b22f5f4eb4af861a4a19657bb12023-02-08T07:57:58ZengIran Telecom Research CenterInternational Journal of Information and Communication Technology Research2251-61072783-44252019-12-011145765A New Framework for Discovering Important Posts and Influential Users in Social NetworksLeila Rabiei0Mojtaba Mazoochi1Farzaneh Rahmani2 ICT Research Institute ICT Research Institute ICT Research Institute The popularity of social networks has rapidly increased over the past few years. Social networks provide many kinds of services and benefits to their users like helping them to communicate, click, view and share contents that reflect their opinions or interests. Detecting important contents defined as the most visited posts and users whom disseminate them can provide some interesting insights from cyberspace user’s activities. In this paper, a framework for discovering important posts (most popular posts by views count) and influential users is introduced. The proposed framework employed on Telegram instant messaging service in this study but it is also applicable to other social networks such as Instagram and Twitter. This framework continuously works in a real social network analysis system named Zekavat to find daily important posts and influential users. The effectiveness of this framework was shown in experiments. The accuracy achieved in the advertisement detection model is 89%. Text-based clustering part of the framework was tested based on the human factor verification and clustering time is less than linear. Graph creation based on publishing relationships is more effective than mention relationship and in this process influential users can be identified in a precise manner.http://ijict.itrc.ac.ir/article-1-448-en.htmlsocial networksclusteringlshmachine learningimportant postsinfluential users |
| spellingShingle | Leila Rabiei Mojtaba Mazoochi Farzaneh Rahmani A New Framework for Discovering Important Posts and Influential Users in Social Networks International Journal of Information and Communication Technology Research social networks clustering lsh machine learning important posts influential users |
| title | A New Framework for Discovering Important Posts and Influential Users in Social Networks |
| title_full | A New Framework for Discovering Important Posts and Influential Users in Social Networks |
| title_fullStr | A New Framework for Discovering Important Posts and Influential Users in Social Networks |
| title_full_unstemmed | A New Framework for Discovering Important Posts and Influential Users in Social Networks |
| title_short | A New Framework for Discovering Important Posts and Influential Users in Social Networks |
| title_sort | new framework for discovering important posts and influential users in social networks |
| topic | social networks clustering lsh machine learning important posts influential users |
| url | http://ijict.itrc.ac.ir/article-1-448-en.html |
| work_keys_str_mv | AT leilarabiei anewframeworkfordiscoveringimportantpostsandinfluentialusersinsocialnetworks AT mojtabamazoochi anewframeworkfordiscoveringimportantpostsandinfluentialusersinsocialnetworks AT farzanehrahmani anewframeworkfordiscoveringimportantpostsandinfluentialusersinsocialnetworks AT leilarabiei newframeworkfordiscoveringimportantpostsandinfluentialusersinsocialnetworks AT mojtabamazoochi newframeworkfordiscoveringimportantpostsandinfluentialusersinsocialnetworks AT farzanehrahmani newframeworkfordiscoveringimportantpostsandinfluentialusersinsocialnetworks |