Identifying Entrepreneurial Influencers on Twitter (original) (raw)
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International Journal of Interactive Mobile Technologies (iJIM)
Social media are widely used as communication platforms in the world of business. Twitter, in particular, offers valuable opportunities for collaboration due to its open nature. For that, many entrepreneurs employ Twitter for different reasons, such as mobilizing financial resources, get funding, and increase their innovation capabilities. Therefore, they keep looking for local entrepreneurial accounts to help them. Messages from entrepreneurial influencersopinion leader-increase the information diffusion to entrepreneurs, helping them to find more opportunities. Discovering the characteristics of entrepreneurial influencers in Twitter networks becomes extremely important since it reflects the way to reach entrepreneurs. In the present paper, we propose a novel framework called ECharacterize based on feature selections techniques to discover the characteristics of the entrepreneurial influencer in the Saudi context in a robust manner. The framework extracts abundant influencers' features and then employs seven state-of-the-art ranking methods to determine the characteristics of the most relevant influencer. It robustly aggregates the lists to come out with the accurate final list using Robust Rank Aggregation. The framework examined on 233,018 real-life Arabic tweets. The results show the ability of the proposed method to distinguish between the influencers by their popularity, reliability and activity level.
Identifying topical influencers on twitter based on user behavior and network topology
Knowledge-Based Systems, 2018
Social media web sites have become major media platforms to share personal information, news, photos, videos and more. Users can even share live streams whenever they want to reach out to many other. This prevalent usage of social media attracted companies, data scientists, and researchers who are trying to infer meaningful information from this vast amount of data. Information diffusion and maximizing the spread of words is one of the most important focus for researchers working on social media. This information can serve many purposes such as; user or content recommendation, viral marketing, and user modeling. In this research, finding topical influential/authority users on Twitter is addressed. Since Twitter is a good platform to spread knowledge as a word of mouth approach and it has many more public profiles than protected ones, it is a target media for marketers. In this paper, we introduce a novel methodology, called Personalized PageRank , that integrates both the information obtained from network topology and the information obtained from user actions and activities in Twitter. The proposed approach aims to determine the topical influencers who are experts on a specific topic. Experimental results on a large dataset consisting of Turkish tweets show that using user specific features like topical focus rate, activeness, authenticity and speed of getting reaction on specific topics positively affects identifying influencers and lead to higher information diffusion. Algorithms are implemented on a distributed computing environment which makes high-cost graph processing more efficient.
Identifying the Topic-Specific Influential Users and Opinion Leaders in Twitter
Artificial Intelligence and Applications / 794: Modelling, Identification and Control / 795: Parallel and Distributed Computing and Networks / 796: Software Engineering / 792: Web-based Education, 2013
The content in online communities has become strategically important that the identification of the influential members can benefit all in developing business opportunities, forging political agendas, discussing social and societal issues, and can lead to many interesting innovative applications. Among social networking websites, one of the most important is Twitter. We are interested in identifying the topic-specific influential members in Twitter. Influence of a user may be determined by many factors. The objective of the study is to identify the factors that play a crucial role in the measurement of a Twitter user's influence, examine how the different factors impact the influence ranking and how these factors can be expressed in a mathematical model. We devise a model to score the strength of a user's influence using basic user features collected for a group of users discussing a common issue. We then validate our results using Klout as a reference point, and then further verify our ranking of the users by manual evaluation.
Content-Based Discovery of Twitter Influencers
2015
Identifying social media influencers in a given domain is considered key to building a brand’s reputation. Influencers are opinion makers who play a critical role in determining the dynamics with which information spreads across a social network. In Twitter, a large number of followers is considered a fundamental indicator to discover influencers. The assumption is that a user with a large number of followers has a large audience and, thus, is more likely to influence the opinion of people in any given domain. Our claim is that influencers can exert an influence only when the content that they share is considered interesting by their followers. In this paper, we propose a content-based measure of influence, called COAX that includes, but is not limited to the number of followers. COAX is tested on a sample of over 10.000 users from random domains according to the Analytic Hierarchy Process (AHP). Preliminary results show how COAX can provide a ranking that is significantly different...
A Methodology for Identifying Influencers and their Products Perception on Twitter
Proceedings of the 20th International Conference on Enterprise Information Systems, 2018
The massive amount of information posted by twitterers is attracting growing interest because of the several applications fields it can be utilized, such as, for instance, e-commerce. In fact, tweets enable users to express opinions about products and to influence other users. Thus, the identification of social network key influencers with their products perception and preferences is crucial to enable marketers to apply effective techniques of viral marketing and recommendation. In this paper, we propose a methodology, based on multilinear algebra, that combines topological and contextual information to identify the most influential twitterers of specific topics or products along with their perceptions and opinions about them. Experiments on a real use case regarding smartphones show the ability of the proposed methodology to find users that are authoritative in the social network in expressing their views about products and to identify the most relevant products for these users, along with the opinions they express.
Identifying ���Influencers��� on Twitter
Word-of-mouth diffusion of information is of great interest to planners, marketers and social network researchers alike. In this work we investigate the attributes and relative influence of 1.6M Twitter users by tracking 39 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. We find that the largest cascades tend to be generated by users who have been influential in the past and from URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk. However, individual-level predictions of which user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects. Finally, we consider a family of hypothetical marketing strategies, and find that under a wide range of plausible assumptions the most cost-effective performance can be realized using "ordinary influencers"-individuals who exert average or even less-than-average influence.
Identifying the Topic-Specific Influential Users in Twitter
International journal of computer applications, 2018
Social Influence can be described as the ability to have an effect on the thoughts or actions of others. Influential members in online communities are becoming the new media to market products and sway opinions. Also, their guidance and recommendations can save some people the search time and assist their selective decision making. The objective of this research is to detect the influential users in a specific topic on Twitter. From a collection of tweets matching a specified query, the influential users are to be detected in an online fashion. In order to address this, the issue of which set of features can best lead us to the topic-specific influential users is investigated along with how these features can be expressed in a model to produce a list of ranked influential users.
Twitterrank: finding topic-sensitive influential twitterers
2010
Abstract This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called" following", in which each user can choose who she wants to" follow" to receive tweets from without requiring the latter to give permission first.
Jurnal IPTEK-KOM, 2023
The topic of start-ups and entrepreneurship has been widely discussed on social media to disseminate information while serving as a digital marketing platform. Unfortunately, researchers and practitioners interested in analyzing social networks to gain business advantage through digital platforms still need to be expanded. This paper analyzes networks on social media to find important actors and opinion leaders on trending start-up and entrepreneurship topics. Data analytics is performed using one of the most extensive social media, Twitter. Several visualizations of the social network of these actors are then displayed using specific network visualization applications. The results are discussed by analyzing the main attributes in the network, namely degree, centrality, modularity, and word cloud, which can lead us to discover important attributes in the network: main actors, opinion leaders, groupings of actors according to topics discussed, and specific terms that are trending topics. This paper contributes to digital marketing practitioners and social network analysis studies in related fields with a concise and duplicable methodology. Further research is recommended to work more extensively with more representative data from multiple networks to analyze these topics in more depth.
Measuring Influence on Twitter using Text and User Relationships
Research in Computing Science
Graph theory concepts as centrality measure can be used to identify users, modelled as nodes of a graph, that have more influence or popularity in a social network. That can be used to classify users. Centrality is one of the most studied concepts in the analysis of Social Networks and there are a great variety of ways to measure it in order to identify the most relevant users in such networks. One of the main issues is how these measures can be calculated in a computationally tractable way and to allow users to be classified as closely as possible to reality. In the literature it can be found many interesting articles that study the application of the aforementioned measures in social networks with millions of users and an enormous amount of messages that flow in those networks. In the present article we are going to combine the information given by the mentioned graph theory measures with text analysis tools to improve the detection of influential users in the Twitter Social Network.