Table 2.

Classification of identifying influencers

Nodes type	Qualification	Identification Process	Related research	Data	Results
Trendsetters	Support and propagate influenced thoughts before becoming famous	- Topic of interest. - Ranking algorithms	- Cervellini et al. (2016) - Saez-Trumper et al. (2012)	- Yelp Dataset - Twitter	- Performs better - The ability to locate a large fraction of trendsetters
Popular Contents	Contents of Popular and Famous People	Random walk model	- Ding et al. (2015)	Twitter	Better than PageRank method
Influencers	Influence personality or attitude	- Topic modeling approach manage textual signal and machine learning algorithms - Using freely available data - Integration of the social capital and social exchange theories - A method to maximize public participation and build smart cities - SNA metrics (DC, CC, BC) - A method based on their social capital value. - Random sampling -Greedy algorithms	- Rodríguez-Vidal et al. (2019) - Harrigan et al. (2021) - Chia et al. (2021) - Kaple et al. (2017) - Pudjajana et al. (2018) - Subbian et al. (2014) - Tsugawa and Kimura (2018) - Sunil and Lingam (2019)	- Twitter - Twitter - General - General - Hoax dataset. - DBLP network - Twitter-follow - SNAP	- Perform above the average - Decision-makers - The structural and relational dimensions influenced SMIs’ propensity. - Good performance - Proved - Outperforms PageRank, PMIA and Weighted Degree baselines up to 8 % in terms of precision, recall, and F1-measure. - Possible benefit - Optimal
Prophets	Knowledgeable and strong capacity to predict the future	Keywords or phrases describing topics or events	- Zhang et al. (2015)	General	Outperform
Influential nodes	Increase an interest propagation process’s asymptotic reach.	Centrality metrics	- Zhou et al. (2019)	General	Optimal late-time
Influential users	Authoritative actors	- WCI integrates 10 different elements into two - Top 1% following growth. - Neighbors’ adoption delays, and the spreading - A multi-features model-based and ranks impact using the Page-Rank concept	- Jain and Sinha (2020) - Yang et al. (2019) - Sheikhahmadi et al. (2017) - Sun et al. (2016)	-Twitter - Twitter - Twitter - Sina Weibo	-The most followers or the highest number of tweets - Proved -Outperforms(speed and capacity) -More powerful
Most/High Influential Users	The top-k users	- Degree Centrality and Page Rank Centrality - An algorithm is interested in the messages - Interactions - High influence user discovery algorithm	- Erlandsson et al. (2016) - Zareie et al. (2019) - Sun and Ng (2012) - Zhao et al. (2019)	- Facebook - General - Facebook and Twitter - Microblogging	- A lower execution time - Effectiveness - Proved - Best performance
Micro-influencers	A special kind of influencers, who are harder to find, less famous but with a higher engagement power over their communities	Automatically approach and highlight personality traits and community values, by analyzing their writings	Leonardi and Monti (2020)	Twitter dataset	Best performing -Impact
Influential Actors	Tweets generate an enormous number of retweets	- Novel influence degree. - The attractiveness model is defined with the T measure	- Qasem et al. (2015) - Qasem et al. (2017)	- Asterisk -Twitter	-Proved -Optimal
Topical influencers	Experts on a given topic	Related features and network feature information. -Based on the language attention network and influence convolution network -Unified hypergraph to model users, images, and various types of relations	- Alp and Ögüdücü (2018) - Zheng et al. (2020) - Fang et al. (2014)	Turkish tweets -Twitter -Twitter	- More efficient. - Comprehensive -Effectiveness and improve the performance
Airline Influencers	influencers relevant to the brands of several airline companies	SNA-based approach	Izdihardian and Ruldeviyani (2021)	Tweets of Indonesian Airline	Important
Instafamous	Instagram celebrities	Test the effects of celebrities	- Jin and Ryu (2020)	Instagram	Discusses theoretical implications
Lead or Follow	Mobilizers and propagators	Systematic literature review	- Florian, 2013 Probst (2013)	OSN data set	Discussion
Consumer Profiles	Consumer of products or services	Analyzing noisy social media data and messages	Hernandez et al. (2013)	Twitter	A 10% random sample
Partisan Slant	Political people	A natural language processing algorithm to analyze at scale the linguistic markers	Karamshuk et al. (2016)	Twitter	Inferred with an accuracy of 60-77%
Pathogenic	As terrorist supporters exploit large communities of supporters for conducting attacks on social media.	A classification algorithm to classify accounts	Alvari et al. (2018)	Twitter	F1-score of 0.6 and via supervised learning identified 71% of the PSMs
Central nodes	Centrality is the most prominent measure that shows the node importance from the information flow standpoint	DICeNod, the compressive sensing-based framework	Mahyar et al. (2018)	Real networks from SNAP	Very well in terms of number of correctly identified central nodes
Influential mavens	Influencer marketing	user network, user behaviour, message readability, and message structure	Harrigan et al. (2021)	Twitter	Effectiveness and efficiency
The Most Influential Spreaders	spread of important information (highly beneficial or alarming if false content)	UACD, a novel method combining both user-specific and topological information	Adnan et al. (2022)	Amazon EC2	Scalable and can process a very large network
Critical nodes	Whose removal reduced the flow of hate	Analysed the graph characteristics then classified as containing evidence of hateful content	Alorainy et al. (2022)	A collected and large dataset	Effectiveness