Discovering influential users in social network using weighted cumulative centrality

2021 ◽  
Author(s):  
Somya Jain ◽  
Adwitiya Sinha
Keyword(s):  
Social Network ◽  
Influential Users

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4258-4265
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Linear Threshold ◽  
The Social ◽  
Spread Of Influence ◽  
Influential Users ◽  
High Influence ◽  
Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

scholarly journals You followed my bot! Transforming robots into influential users in Twitter

First Monday ◽  
2013 ◽  
Author(s):  
Johnnatan Messias ◽  
Lucas Schmidt ◽  
Ricardo Oliveira ◽  
Fabrício Benevenuto
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Two Systems ◽  
Scientific Papers ◽  
The Media ◽  
Influential Users

Systems that classify influential users in social networks have been used frequently and are referenced in scientific papers and in the media as an ideal standard of evaluation of influence in the Twitter social network. We consider such systems of evaluation to be complex and subjective, and we therefore suspect that they are vulnerable and easy to manipulate. Based on this, we performed experiments and analysis of two systems for ranking influence: Klout and Twitalyzer. We created simple robots capable of interacting by means of Twitter accounts, and we measured how influent they were. Our results show that it is possible to become influential through simple strategies. This suggests that the systems do not have ideal means to measure and classify influence.

scholarly journals Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 709
Author(s):  
Khurshed Ali ◽  
Cheng-Te Li ◽  
Yi-Shin Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Maximization Problem ◽  
Target Region ◽  
Specific Location ◽  
Influential Users ◽  
Location Based Social Networks ◽  
Location Type ◽  
Selection Of ◽  
Product Promotion

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location’s popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

Influential Users in Social Network Services

2018 ◽  
Vol 49 (1) ◽  
pp. 13-32 ◽  
Author(s):  
Young-Kyu Kim ◽  
Dongwon Lee ◽  
Janghyuk Lee ◽  
Ji-Hwan Lee ◽  
Detmar W. Straub
Keyword(s):  
Social Network ◽  
Network Services ◽  
Social Network Services ◽  
Influential Users

Identifying Influential Users in Twitter Networks of the Turkish Diaspora in Belgium, the Netherlands, and Germany

2020 ◽  
pp. 235-263
Author(s):  
Roya Imani Giglou ◽  
Leen d'Haenens ◽  
Baldwin Van Gorp
Keyword(s):  
Social Network ◽  
The Netherlands ◽  
Structural Changes ◽  
Social Medium ◽  
Centrality Measures ◽  
Network Centrality ◽  
Social Network Centrality ◽  
Influential Users

This study investigates how members of the Turkish diaspora connected online using Twitter as a social medium during the Gezi Park protests and how those connections and the structure of the resulting Twitter network changed after the protests ended. Further, the authors examine respondents' online influence and their roles in the movement, using social network centrality measures and Tommasel and Godoy's (2015) novel metric. The authors utilize data from Twitter to determine the connections between 307 distinct users, using both online and offline surveys. The findings reveal that Turkish diaspora members' use of Twitter provided the impetus for larger structural changes to the Twitter network. Moreover, results indicate that users' influence was not related to the frequency of their re-tweets or the number of their Twitter followers. Rather, users' influence corresponds to other factors such as their ability to spread information and engage with other users and also to the importance of their Twitter content.

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