Analysis of Online Social Network Connections for Identification of Influential Users

Al-Garadi, Mohammed Ali; Varathan, Kasturi Dewi; Ravana, Sri Devi; Ahmed, Ejaz; Mujtaba, Ghulam; Khan, Muhammad Usman Shahid; Khan, Samee U.

doi:10.1145/3155897

Cited by 102 publications

(74 citation statements)

References 173 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, the proposed measure is capable to find more reliable influential nodes than degree centrality and page rank and with robustness in predicting information spreaders, it enables the nodes of a network to attain higher influence ratio. Further literature on the invalidity of degree centrality and page ranking measures can be found in 48,49 . Thus, the initial fraction with 10% threshold is among the best in estimating the topmost knowledge spreaders.…”

Section: Abstract Classification For Model Feasibilitymentioning

confidence: 99%

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits

Agarwal

Toshniwal

2020

Sci Rep

View full text Add to dashboard Cite

With the availability of smart devices and affordable data plans, social media platforms have become the primary source of information dissemination across geographically dispersed users/locations. it has shown great potential across different application domains including event detection, opinion analysis, recommendation, and prediction. However, the process of extracting useful information from the collected voluminous social media data during natural hazards is a standing problem that needs significant attention from the research community. The fine-grained knowledge detailing users' participation in information spreading could be advantageous in developing a reliable social network for the adverse events (Natural Hazards, Man-made attacks etc.). However, there has been no such findings related to identification of leader and their leadership characteristics associated with natural hazards in previous studies. We have collected 20.6 million tweets which were posted by 5.3 million users, during distinct devastating hazards namely-Floods, Hurricane, Earthquake and Typhoons. To achieve the goal, we divided our work in to three parts. Firstly, classify the collected crises data into four domains i.e resource, causality, news, and sympathy by employing deeper recurrent neural network model. Secondly, we used statistical physics of complex network to recognize local as well as global prominent leaders. At last, we curate leadership characteristics in terms of their big five personality traits and emotional traits. Our experimental, results find evidence that local leadership behaviour characteristics are significantly different from global potentials. Where as we also finds that some behaviour traits were certain to classified domains (resource, causality, news, and sympathy) and some were certain to hazard divisions, though emotional characteristics remained consistent. Later, we conclude that local potentials leaders have comparatively higher emotional strength. Furthermore, when the complete local network structure is unavailable, we find that the dynamic rank is reliable indexing proxy for local potentials. The current study, provide useful insight to understand how leadership characteristics are influenced to hazards, domains and centrality of users. Nowadays, billions of active users over worldwide use social network platforms (SNPs) to interact with each other and shares their day to day activity over platforms like Twitter, Facebook, Tumbler and many more. SNPs have an ample amount of real-time information sources pertinent to real-world events, predominately during any natural hazards or mass crises. These platforms play a vital role to disseminate any information among users through online social interaction 1-3. The previous study on information dissemination reveals that few influential knowledge spreaders (IKs) have the characteristics that can shape millions of users viewpoint 4. Therefore identification of IKs holds immense practical importance and also in recent years it attracted the considerable at...

show abstract

Section: Abstract Classification For Model Feasibilitymentioning

confidence: 99%

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits

Agarwal

Toshniwal

2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The identification of the influential users in social networks is an important process for either accelerating the dissemination of information containing such as scientific messages, marketing applications, political movements and recruitment, or preventing the dissemination of undesired contents, such as spams, viruses, negative online behaviors, and grapevine 76 . Influential users are usually recognized by their ranking relative to topological measures, and therefore, an efficient topological measurement algorithm is essential for identifying influential users in social networks 77 . Recently, this issue has attracted considerable attention, and many new studies have been published.…”

Section: Fundamentals Of Snamentioning

confidence: 99%

“…It builds a graph network using users as nodes and retweets as edges between users. Researchers have also suggested a variety of algorithms such as InfluenceRank, LeaderRank, TwitterRank, ProfileRank, and Degree Descending Search Evolution (DDSE) algorithm to identify influential users and influence maximization 77,86 …”

Section: Fundamentals Of Snamentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

View full text Add to dashboard Cite

Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

show abstract

“…Al-Garadi et al 2018 warns us that "online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas [17]. The identification of the influential users in OSNs is a significant process for accelerating the propagation of information that includes marketing applications or hindering the dissemination of unwanted contents, such as viruses, negative online behaviors, and rumors."…”

Section: Conceptual Model Of the Open Data Systemmentioning

confidence: 99%

Open data – an introduction to the issue

Dymora

Mazurek

2018

ITM Web Conf.

View full text Add to dashboard Cite

Rapidly developing of internet technologies and digitalization of government generate more and more data. Databases from various public institutions and private sectors, e.g. in the fields of economics, transport, environment and public safety are publishing in the global Internet network, so that any user can browse them without additional charges. Most of this data is published on the open data portals. Open data - that is, “open”, public data can allow the processing and analysis of information contained in them completely free of charge. This article is an introduction to a fairly new area of issues such as “open data” or “open government”, presents the main mechanisms of accessing to data in public open data portals and also propose a conceptual open data/government model.

show abstract

Analysis of Online Social Network Connections for Identification of Influential Users

Cited by 102 publications

References 173 publications

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits

Semantic analysis on social networks: A survey

Open data – an introduction to the issue

Contact Info

Product

Resources

About