Document Type : Research Article


1 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

2 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

3 Quaid-i-Azam University, Islamabad, Pakistan.


Entrepreneurship involves an immense network of activities, linked via collaborations and information propagation. Information dissemination is extremely important for entrepreneurs. Finding influential users with high levels of interaction and connectivity in social media and involving them in information spread helps disseminating the information quickly. Thus, facilitating key entrepreneurial actors to find and collaborate with each other. Identifying and ranking entrepreneurial top influential people is still in infancy. This paper proposes an ERank framework for topic-specific influence theories that are specialized with respect to Twitter. Firstly, it extracts four dimensions to characterize influencers, including user popularity, activity, reliability, and tweet quality. Afterwards, it uses linear combinations of these dimensions to assign influence score to each user. Experimental results on a real-life dataset containing 233,018 Arabic tweets show that ERank successfully ranks 8 out of 10 entrepreneurial influencers. Unlike other existing approaches, ERank doesn’t require any labelled data and has lower computational cost. To ensure the effectiveness and efficiency of ERank, three validation techniques were used (1) to compare the detected influencers with the real-world influencers, (2) to investigate the spread of information of the detected influencers, and (3) to compare the quality of ERank results with other ranking methods.


[1] Conor Drummond, Helen McGrath, and Thomas O’Toole. The impact of social media on resource mobilisation in entrepreneurial firms. Industrial Marketing Management, 70:68–89, 2018.
[2] Daria J Kuss and Mark D Griffiths. Social networking sites and addiction: Ten lessons learned. International journal of environmental research and public health, 14(3):311, 2017.
[3] Eun Kyung Park, Raphael Mateus Martins, Daniel Hain, and Roman Jurowetzki. Entrepreneurial ecosystem for technology start-ups in nairobi: Empirical analysis of twitter networks of start-ups and support organizations. In DRUID, 2017.
[4] I Fiegenbaum and O Mohout. The power of twitter: Building an innovation radar using social media. In Proceedings of the XXVI ISPIM Conference–Shaping the Frontiers of Innovation Management, pages 1–17, 2015.
[5] Daniel A Gruber, Ryan E Smerek, Melissa C Thomas-Hunt, and Erika H James. The realtime power of twitter: Crisis management and leadership in an age of social media. Business Horizons, 58(2):163–172, 2015.
[6] Jan H Kietzmann, Kristopher Hermkens, Ian P McCarthy, and Bruno S Silvestre. Social media? get serious! understanding the functional building blocks of social media. Business horizons, 54(3): 241–251, 2011.
[7] Eileen Fischer and A Rebecca Reuber. Online entrepreneurial communication: Mitigating uncertainty and increasing differentiation via twitter. Journal of Business Venturing, 29(4):565–583, 2014.
[8] Philip Gratell and Carl Johan Dahlin. How does social media affect entrepreneurial leadership: A qualitative study on entrepreneurs perceptions regarding social media as a tool for entrepreneurial leadership, 2018.
[9] Sagar S De, Satchidananda Dehuri, and Gi-Nam Wang. Machine learning for social network analysis: A systematic literature review. IUP Journal of Information Technology, 8(4), 2012.
[10] Nicholas A Christakis and James H Fowler. The spread of obesity in a large social network over 32 years. New England journal of medicine, 357 (4):370–379, 2007.
[11] Yu Zhang and Yu Wu. How behaviors spread in dynamic social networks. Computational and Mathematical Organization Theory, 18(4):419– 444, 2012.
[12] XD Wu, Yi Li, and Lei Li. Influence analysis of online social networks. Chinese journal of Computers, 37(4):735–752, 2014.
[13] Duncan J Watts and Steven H Strogatz. Collective dynamics of small-worldnetworks. nature, 393(6684):440–442, 1998.
[14] Bernard J Jansen, Mimi Zhang, Kate Sobel, and Abdur Chowdury. Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60 (11):2169–2188, 2009.
[15] Mani R Subramani and Balaji Rajagopalan. Knowledge-sharing and influence in online social networks via viral marketing. Communications of the ACM, 46(12):300–307, 2003.
[16] Elihu Katz and Paul Felix Lazarsfeld. Personal Influence, The part played by people in the flow of mass communications. Transaction publishers, 1966.
[17] Isabel Anger and Christian Kittl. Measuring influence on twitter. In Proceedings of the 11th international conference on knowledge management and knowledge technologies, pages 1–4, 2011.
[18] Mohamed Bouguessa and Lotfi Ben Romdhane. Identifying authorities in online communities. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3):1–23, 2015.
[19] Daniel Gayo-Avello. Nepotistic relationships in twitter and their impact on rank prestige algorithms. Information Processing & Management, 49(6):1250–1280, 2013.
[20] Wen Chai, Wei Xu, Meiyun Zuo, and Xiaowei Wen. Acqr: A novel framework to identify and predict influential users in micro-blogging. In Pacis, page 20, 2013.
[21] Nian Liu, Lin Li, Guandong Xu, and Zhenglu Yang. Identifying domain-dependent influential microblog users: A post-feature based approach. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 28, 2014. [22] Shao Xianlei, Zhang Chunhong, and Ji Yang. Finding domain experts in microblogs. ser. WEBIST, 14, 2014.
[23] Lamjed Ben Jabeur, Lynda Tamine, and Mohand Boughanem. Active microbloggers: Identifying influencers, leaders and discussers in microblogging networks. In International Symposium on String Processing and Information Retrieval, pages 111– 117. Springer, 2012.
[24] Jingxuan Li, Wei Peng, Tao Li, Tong Sun, Qianmu Li, and Jian Xu. Social network user influence sense-making and dynamics prediction. Expert Systems with Applications, 41(11):5115– 5124, 2014.
[25] Miguel del Fresno Garcia, Alan J Daly, and Sagrario Segado Sanchez-Cabezudo. Identifying the new influences in the internet era: Social media and social network analysis. Revista Espa˜nola de Investigaciones Sociol´ogicas, (153), 2016.
[26] MS Srinivasan, Srinath Srinivasa, and Sunil Thulasidasan. Exploring celebrity dynamics on twitter. In Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop, pages 1–4, 2013.
[27] Gerasimos Razis and Ioannis Anagnostopoulos. Influencetracker: Rating the impact of a twitter account. In IFIP International Conference on Artificial Intelligence Applications and Innovations, pages 184–195. Springer, 2014. [28] Daniele Quercia, Jonathan Ellis, Licia Capra, and Jon Crowcroft. In the mood for being influential on twitter. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing, pages 307–314. IEEE, 2011.
[29] Stefan R¨abiger and Myra Spiliopoulou. A framework for validating the merit of properties that predict the influence of a twitter user. Expert Systems with Applications, 42(5):2824–2834, 2015.
[30] AN Arularasan, Annamalai Suresh, and Koteeswaran Seerangan. Identification and classification of best spreader in the domain of interest over the social networks. Cluster Computing, 22 (2):4035–4045, 2019.
[31] Rafael Cappelletti and Nishanth Sastry. Iarank: Ranking users on twitter in near real-time, based on their information amplification potential. In 2012 International Conference on Social Informatics, pages 70–77. IEEE, 2012.
[32] Manuel Castriotta, Michela Loi, Elona Marku, and Luca Naitana. Whats in a name? exploring the conceptual structure of emerging organizations. Scientometrics, 118(2):407–437, 2019.
[33] Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and Krishna Gummadi. Measuring user influence in twitter: The million follower fallacy. In Proceedings of the International AAAI Conference on Web and Social Media, volume 4, 2010.
[34] Jinyoung Kim. How did the information flow in the# alphago hashtag network? a social network analysis of the large-scale information network on twitter. Cyberpsychology, Behavior, and Social Networking, 20(12):746–752, 2017.
[35] Songjun Ma, Ge Chen, Luoyi Fu, Weijie Wu, Xiaohua Tian, Jun Zhao, and Xinbing Wang. Seeking powerful information initial spreaders in online social networks: a dense group perspective. Wireless Networks, 24(8):2973–2991, 2018.
[36] Amir Sheikhahmadi and Mohammad Ali Nematbakhsh. Identification of multi-spreader users in social networks for viral marketing. Journal of Information Science, 43(3):412–423, 2017.
[37] Igors Skute. Opening the black box of academic entrepreneurship: a bibliometric analysis. Scientometrics, 120(1):237–265, 2019.
[38] Hong-liang Sun, Eugene Chng, and Simon See. Influential spreaders in the political twitter sphere of the 2013 malaysian general election. Industrial Management & Data Systems, 2019.
[39] Ramine Tinati, Leslie Carr, Wendy Hall, and Jonny Bentwood. Identifying communicator roles in twitter. In Proceedings of the 21st International Conference on World Wide Web, pages 1161–1168, 2012.
[40] Zeynep Zengin Alp and S¸ule G¨und¨uz O˘g¨ud¨uc¨u. ¨ Identifying topical influencers on twitter based on user behavior and network topology. KnowledgeBased Systems, 141:211–221, 2018.
[41] Kechen Zhuang, Haibo Shen, and Hong Zhang. User spread influence measurement in microblog. Multimedia Tools and Applications, 76(3):3169– 3185, 2017.
[42] Jean-Val`ere Cossu, Nicolas Dugu´e, and Vincent Labatut. Detecting real-world influence through twitter. In 2015 Second European Network Intelligence Conference, pages 83–90. IEEE, 2015.
[43] Abolfazl Aleahmad, Payam Karisani, Maseud Rahgozar, and Farhad Oroumchian. Olfinder: Finding opinion leaders in online social networks. Journal of Information Science, 42(5):659–674, 2016.
[44] Mehran Asadi and Afrand Agah. Characterizing user influence within twitter. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pages 122–132. Springer, 2017.
[45] Jinfeng Yuan, Li Li, Le Luo, and Min Huang. Topology-based algorithm for users’ influence on specific topics in micro-blog. JOURNAL OF INFORMATION &COMPUTATIONAL SCIENCE, 10(8):2247–2259, 2013.
[46] Jonny Bentwood. Distributed influence: Quantifying the impact of social media. Hentet den, 15, 2008.
[47] Jean-Val`ere Cossu, Vincent Labatut, and Nicolas Dugu´e. A review of features for the discrimination of twitter users: Application to the prediction of offline influence. Social Network Analysis and Mining, 6(1):25, 2016.
[48] Veneta Andonova, Milena S Nikolova, and Dilyan Dimitrov. What is an entrepreneurial ecosystem? Entrepreneurial Ecosystems in Unexpected Places, pages 3–16, 2019.
[49] Mark EJ Newman. Power laws, pareto distributions and zipf’s law. Contemporary physics, 46 (5):323–351, 2005.
[50] Fabi´an Riquelme and Pablo Gonz´alezCantergiani. Measuring user influence on twitter: A survey. Information processing & management, 52(5):949–975, 2016.
[51] Martin J Chorley, Gualtiero B Colombo, Stuart M Allen, and Roger M Whitaker. Human content filtering in twitter: The influence of metadata. International Journal of Human-Computer Studies, 74:32–40, 2015.
[52] A Famili, Wei-Min Shen, Richard Weber, and Evangelos Simoudis. Data preprocessing and intelligent data analysis. Intelligent data analysis, 1(1):3–23, 1997.
[53] S Patro and Kishore Kumar Sahu. Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462, 2015.
[54] Robert Gilmore Pontius Jr and Marco Millones. Death to kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15):4407–4429, 2011.
[55] Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu. A theoretical analysis of ndcg ranking measures. In Proceedings of the 26th annual conference on learning theory (COLT 2013), volume 8, page 6, 2013. [56] Scikitlearn. stable/modules-/generated/sklearn.svm. libsvm.predict_proba.html, Accessed 22 December 2019.