Identifying Social Media Influencers using Graph Based Analytics
AUTHORS
Pankti Joshi,Computer Science Department, Lakehead University, Ontario, Canada
Sabah Mohammed*,Computer Science Department, Lakehead University, Ontario, Canada
ABSTRACT
Social network analysis has been an essential topic with broad content sharing from social media. Defining the directed links in social media determine the flow of information and indicates the user’s influence. Due to the enormous data and unstructured nature of sharing information, there are several challenges caused while handling data. Graph Analytics proves to be an essential tool for addressing problems such as building networks from unstructured data, inferring information from the system, and analyzing the community structure of a network. The proposed approach aims to determine the influencers on Twitter data, based on the follower’s links as well as the retweet links. Several graph-based algorithms are implemented on the data collected to find the influencer as well as conversation communities in the network of twitter users.
KEYWORDS
Social media, Twitter, Influencers, Graph analytics, Graph database
REFERENCES
[1] Lutu Patricia E. Nalwoga, “Using twitter mentions and a graph database to analyse social network centrality.” In 2019 6th International Conference on Soft Computing Machine Intelligence (ISCMI), pp.155-159, IEEE, (2019)
[2] Werayawarangura, Nattapon, Thanaphoom Pungchaichan, and Peerapon Vateekul, “Social network analysis of calling data records for identifying influencers and communities,” In 2016 13th International Joint Confer-ence on Computer Science and Software Engineering (JCSSE), pp.1-6. IEEE, (2016)
[3] Manickavasagam, Sounthar, and B. Vinayaga Sundaram, “Exploring gender based influencers using Social Network Analysis,” In 2014 Sixth International Conference on Advanced Computing (ICoAC), pp.224-228. IEEE, (2014)
[4] Hu, Yuheng, Shelly Farnham, and Kartik Talamadupula, “Predicting user engagement on twitter with real-world events.” In 9th Int. AAAI Conf. on Web and Social Media, (2015)
[5] Werayawarangura, Nattapon, Thanaphoom Pungchaichan, and Peerapon Vateekul, “Social network analysis of calling data records for identifying influencers and communities,” In 2016 13th International Joint Confer-ence on Computer Science and Software Engineering (JCSSE), pp.1-6. IEEE, (2016)
[6] Tridetti Stephane´, “Social network analysis: detection of influencers in fashion topics on Twitter,” Master Thesis, department of Mathematics, University of Liège, (2016)
[7] Mark Needham, “Finding influencers and communities in the Graph Community,” May 15, (2019)
[8] https://medium.com/neo4j/finding-influencers-and-communities-in-the-graph-community-e3d691296325. (2020)
[9] Crowdflower’s Data for Everyone library, “First GOP Debate Twitter Sentiment Analyze tweets on the first 2016 GOP Presidential Debate,” [Online]. Available: https://www.kaggle.com/crowdflower/first-gop-debate-twitter-sentiment. [Accessed February 10, 2020], (2017)
[10] Kim, Edward Dong-jin, and Brian Jia-lee Keng, “Systems and methods for determining influencers in a social data network,” U.S. Patent Application 14/522,471, filed April 30, (2015)
[11] Francalanci, Chiara, and Ajaz Hussain. “Influence-based Twitter browsing with NavigTweet,” Information Systems 64, pp.119-131, (2017)