The Dynamic User’s Influence In Spreading Information On Twitter | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 25, Issue 1, March 2025, Pages 18-28 PDF (606.04 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2025.359952.1376 | ||
| Authors | ||
| Eman Elsayed Mahmoud* 1; Eman Elsayed Mahmoud2 | ||
| 1school of business technology, Canadian International Collage | ||
| 2School of Business technology , Canadian international collage | ||
| Abstract | ||
| Abstract: Social media became a significant channel for spreading information and linking common interests’ users. Therefore, the users of social media have direct/indirect influence on others, which provides spreading information. This supports the marketers on directing their campaigns through the most influential users. Since, the social influence has a level of uncertainty; this paper focuses on analysing the different criteria that may be helpful in measuring a user’s influence across topics and time. It applied study on 1.5 million tweeters that have been crawled to measure the influential users category and their impact in spreading information on social networks. Mainly, the paper focuses on ranking the influential users based on analysing the correlation between four essential measures; the user’s tweets visibility, participation, retransmission, and reply. Then, the impact of the different user’s category in spreading information has been investigated by topic. The findings emphasized that top ranked influential users have a high percentage of retransmission, participation, and visibility. It found out that popular users (who have high activity and big number of followers) have the highest spreading ratio in the first hour of posting compared to unpopular users. The ratio increased incrementally over the first few hours for 60% up to 100% of influencers. Moreover, the influential user’s experience, and specialty in a specific field is an essential influencing factor in spreading information, where 70% of their tweets are spread in the first hour of posting and increasing incrementally. | ||
| Keywords | ||
| Spreading information; dynamic influence; user influence | ||
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