Influence In Online Social Networks Research Articles

Agglomerative clustering enhanced GA for optimal seed selection in online social networks

Purpose The social media revolution has brought tremendous change in business strategies for marketing and promoting the products and services. Online social networks have become prime choice to promote the products because of the large size of online communities. Identification of seed nodes or identifying the users who are able to maximize the spread of information over the network is the key challenge faced by organizations. It is proved as non-deterministic polynomial-time hard problem. The purpose of this paper is to design an efficient algorithm for optimal seed selection to cover the online social network as much as possible to maximize the influence. In this approach, agglomerative clustering is used to generate the initial population of seed nodes for GA. Design/methodology/approach In this paper agglomerative clustering based approach is proposed to generate the initial population of seed nodes for GA. This approach helps in creating the initial populations of Genetic algorithm from different parts of the network. Genetic algorithm evolves this population and aids in generating the best seed nodes in the network. Findings The performance of of proposed approach is assessed with respect to existing seed selection approaches like k-medoid, k-means, general greedy, random, discounted degree and high degree. The algorithms are compared over networks data sets with varying out-degree ratio. Experiments reveal that the proposed approach is able to improve the spread of influence by 35% as compared to contemporary techniques. Originality/value This paper is original contribution. The agglomerative clustering-based GA for optimal seed selection is developed to improve the spread of influence in online social networks. This paper is of immense importance for viral marketing and the organizations willing to promote product or services online via influential personalities.

Topic-based influential user detection: a survey

Online Social networks have become an easy means of communication for users to share their opinion on various topics, including breaking news, public events, and products. The content posted by a user can influence or affect other users, and the users who could influence or affect a high number of users are called influential users. Identifying such influential users has a wide range of applications in the field of marketing, including product advertisement, recommendation, and brand evaluation. However, the users’ influence varies in different topics, and hence a tremendous interest has been shown towards identifying topic-based influential users over the past few years. Topic-level information in the content posted by the users can be used in various stages of the topic-based influential user detection (IUD) problem, including data gathering, construction of influence network, quantifying the influence between two users, and analyzing the impact of the detected influential user. This has opened up a wide range of opportunities to utilize the existing techniques to model and analyze the topic-level influence in online social networks. In this paper, we perform a comprehensive study of existing techniques used to infer the topic-based influential users in online social networks. We present a detailed review of these approaches in a taxonomy while highlighting the challenges and limitations associated with each technique. Moreover, we perform a detailed study of different evaluation techniques used in the literature to overcome the challenges that arise in evaluating topic-based IUD approaches. Furthermore, closely related research topics and open research questions in topic-based IUD are discussed to provide a deep understanding of the literature and future directions.

Probabilistic reasoning system for social influence analysis in online social networks

People interact with other people in their daily life, either for work or for personal reasons. These interactions are often complex. Thus, interactions that an individual has with other individuals, to some extent, influence the decisions they make. There have been many efforts to uncover, explore, and measure the concept of social influence. Thus, modeling influence is an open and challenging problem where most evaluation models focus on online social networks. However, they fail to characterize some properties of social influence. To address the limitations of the previous approaches, we propose a novel Probabilistic Reasoning system for social INfluence analysis (PRIN) to examine the social influence process and elucidate the factors that affect it in an attempt to explain this phenomenon. In this paper, we present a model that quantitatively measures social influence in online social networks. Experiments on a real social network such as Twitter demonstrate that the proposed model significantly outperforms traditional feature engineering-based approaches. This suggests the effectiveness of this novel model when modeling and predicting social influence.

Measuring Time-Constrained Influence to Predict Adoption in Online Social Networks

Recently, there has been strong interest in measuring influence in online social networks. Different measures have been proposed to predict when individuals will adopt a new behavior, given the influence produced by their friends. In this article, we show that one can achieve significant improvement over these measures, extending them to consider a pair of time constraints that provide a better proxy for social influence. By conducting an engineering study that investigates retweet networks from Twitter and Sina Weibo datasets, we tune those two parameters while we examine the correlation between influence and the probability of adoption, as well as the ability to predict adoption, estimating the real susceptibility and influence to which microblog users are dynamically subjected. Although there are limitations about using retweets to analyze social influence, our results show that for the simple count of active neighbors, its correlation with the probability of adoption is boosted up to 518.75%, whereas similar gains are observed for the other influence measures analyzed. We also obtain up to 18.89% improvement in F 1 score when compared to recent machine learning techniques that aim to predict adoption, enabling practical use of the corresponding concepts for social influence applications.

Topic based time-sensitive influence maximization in online social networks

With the exponential expansion of online social networks (OSNs), an extensive research on information diffusion in OSNs has been emerged in recent years. One of the key research is influence maximization (IM). IM is a problem to find a seed set with k nodes, so as to maximize the range of information propagation in OSNs. Most research adopts general greedy, degree discount, and centrality measures, etc. to find the seed node set. However, the time sensitivity is ignored in terms of propagation delay and duration under some specific situations such as emergent epidemiological bulletins and natural disaster warning. In the meantime, prior studies have mainly based on static network topology, but the user’s online/offline status and the topic preference of interaction make it difficult for traditional methods to be applied well in reality. Considering the above analysis, in this paper, we focus on the problem of time-sensitive influence maximization and give its formal description. Here, a dynamic network is constructed in terms of users’ online status. To solve the problem, we propose a topic based time-sensitive dynamic propagation model considering user topic preference and interaction delay. Besides, two algorithms are used in our work to find a seed set that could maximize the influence in OSNs with the time constraint, namely topic-based time-sensitive greedy algorithm (TTG) and topic-based time-sensitive heuristic algorithm (TTH), respectively. Extensive experiments on Twitter dataset demonstrate the efficiency and influence performance of the proposed algorithms on evaluation metrics.

Susceptibility to social influence predicts behavior on Facebook

Susceptibility to social influence (SSI) has been reported as a key factor for social influence in online social networks (OSNs) such as Facebook, Instagram, and Twitter. In four online studies, we show that the personality trait of SSI, namely the susceptibility to normative influence (SNI), predicts the extent to which Facebook users comply with the behavior of others on Facebook (e.g., buying, voting, or visiting what other OSN users post). In Studies 1a and 1b, we find that SSI correlates with diverse OSN behaviors, which are the typical results of being affected by social influence. In Study 1b, we find that the perceived importance of the topic of OSN behaviors (e.g., fashion or politics) moderates the effect of SNI on OSN behavior, with a higher importance resulting in a stronger effect of SNI on OSN behavior. In Studies 2 and 3, we find that SNI predicts the extent Facebook users hypothetically “like” diverse topics on Facebook. We also find partial support for the idea that there are interactions between SNI and the Big Five personality traits (i.e., openness, conscientiousness, extraversion, agreeableness, and neuroticism) on OSN behavior. Specifically, the extent to which the Big Five personality traits of openness, agreeableness, and neuroticism predict OSN behavior depends on Facebook users’ SNI. Our studies contribute to research on the personality-based prediction of OSN behavior and help in better understanding the dynamics of social influence in OSNs, underlining the vulnerability of susceptible OSN users.

Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review

Online Social Networks (OSNs) are an integral part of the modern lifestyle, and the use of OSN is highly prevalent today. OSNs have enabled the generation of unprecedented quantities of data on users, their interests, relationships, actions, opinions, locations visited, items purchased, and their experiences in various aspects of life. Data and information related to reputation and influence exist on OSNs and are hidden. The objective of this study is to provide a better and deeper understanding and a detailed review of the current state of research pertaining to the measurement of reputation and influence in OSNs, specifically Twitter. We conducted a Systematic Literature Review to collect, analyze, and synthesize data on the accuracy and value of previous literature that has focused on this field, has addressed a variety of topics, and has been published in digital databases between 2010 and September 2019. We followed a predefined review procedure that integrated both automatic and manual search strategies to cover all relevant research papers published in this period.

Modeling influence on posting engagement in online social networks: Beyond neighborhood effects

In many online social networks (OSN), Member A can observe the online participation of Member B and thus may be exposed to a shift in the online participation of B. Such exposure is often modeled by a neighbor-to-neighbor spreading process that can lead to local influence. In other words, exposure to participation shifts of Neighbor B can exert local influence on A’s posting engagement. Since many posts nowadays are public rather than private, exposure to participation shifts of non-neighbors outside A’s neighborhood can exert global influence on A’s posting engagement. We model influence spread in an OSN by differentiating between exposure to participation shifts of local and global influence sources. The results of applying the proposed model to different temporal dynamic OSNs show evidence of both local and global influence on posting engagement of members. This consideration of global influence in OSNs, beyond neighborhood effects, extends past research that focused mainly on local influence and provides new insights about influence sources in general and viral marketing in particular.

Modeling the Consumers Opinion Influence in Online Social Media in the Case of Eco-friendly Products

Social influence has a positive impact on the purchase intention for eco-friendly products along with other subjective and objective aspects related to environmental attitude, product attitude, and subjective and objective knowledge. Also, exposure to media has been proven to have a significant positive affect on environmental attitude, with effect on the purchase intention. Several recent studies have shown the importance of consumers’ influence in online social networks, underlying the role played by the online environments over consumers’ attitude. As a result, the current research tries to analyze the influence exerted on consumers’ decision to purchase eco-friendly products by their activity in online social environments. Using a questionnaire, filled-in by 409 respondents, a series of variables have been extracted with regard to the eco-friendly products. An agent-based model has been created, fed with the values of the variables extracted from the questionnaire, and used for simulations. As a result, it has been observed that an increase in online media exposure can have a high positive impact on the eco-friendly product adoption. Depending on the type of product—soft or durable good—different times for the eco-friendly product adoption have been determined relatively to the considered variables. Last, the possible limitations of using an agent-based modeling approach are discussed, along with possible extensions and improvements.

Topic Influence Analysis Based on User Intimacy and Social Circle Difference

Analyzing topical user influence in online social networks is conducive to better advertisement injection, information dissemination, and user behavior analysis. In this paper, we propose a new approach to measure topical user influence in online social networks. Specifically, by comprehensively considering users' social relationships, posting and forwarding behaviors, and posts content, we define two metrics of user intimacy and social circle difference to measure how influential users rank on different topics. The dataset obtained from Sina Weibo is utilized to evaluate the effects of our proposed approach and several comparison methods. Specifically, the advantages of our approach are evaluated from several aspects, including the correlations between influence rankings on different topics, the spread ability of high influential users, and the spread balance of high influential users. The extensive experimental results show that our approach is superior in mining influential users on different topics. Specifically, in our approach, the user influence rankings on different topics are less correlated, the users with high influence rankings achieve higher spread scope, and the higher a user's influence ranking, the more evenly the user's posts being spread among different communities.

Distance-based customer detection in fake follower markets

As the Online Social Networks (OSNs) have become popular, more and more people want to increase their influence not only in the real world but also in the OSNs. However, increasing the influence in OSNs is time-consuming job, so some users want to find a shortcut to increase their relationships. The demand for quick increasement of relationship has led to the growth of the fake follower markets that cater to customers who want to grow their relationships rapidly. However, customers of fake follower markets cannot manipulate legitimate user’s relationship perfectly. Existing approaches explore node’s relationships or features to detect customers. But none of them combines the relationships and node’s features directly. In this article, we propose a model that directly combines the relationship and node’s feature to detect customers of fake followers. Specifically, we study the geographical distance for 1-hop-directional links using the nodes geographical location. Motivated by the difference of a distance ratio for 1-hop directional links, the proposed method is designed to generate a 1-hop link distance ratio, and classifies a node as a customer or not. Experimental results on a Twitter dataset demonstrate that the proposed method achieves higher performance than other baseline methods.

Disentangling Sources of Influence in Online Social Networks

Information propagation in online social networks is facilitated by two types of influence - endogenous (peer) influence that acts between users of the social network and exogenous (external) that corresponds to various external mediators such as online news media. However, inference of these influences from data remains a challenge, especially when data on the activation of users is scarce. In this paper we propose a methodology that yields estimates of both endogenous and exogenous influence using only a social network structure and a single activation cascade. Our method exploits the statistical differences between the two types of influence - endogenous is dependent on the social network structure and current state of each user while exogenous is independent of these. We evaluate our methodology on simulated activation cascades as well as on cascades obtained from several large Facebook political survey applications. We show that our methodology is able to provide estimates of endogenous and exogenous influence in online social networks, characterize activation of each individual user as being endogenously or exogenously driven, and identify most influential groups of users.

Exploring influence maximization in online and offline double-layer propagation scheme

Information propagation in network environment is a widely studied research topic, especially in Online Social Networks (OSNs), where the problem has gained significant popularity. Recent studies attempt to pick up the key nodes, who could maximize the network influence in OSNs. However, in addition to propagation in OSNs, another information propagation way is through words of mouth among people in the offline mobile network, which is an indispensable factor and is not considered in most cases. Hence, the information propagation in both online social network and offline mobile network is a new valuable scheme. In this paper, we propose an Information Maximization strategy in Online and Offline double-layer Propagation scheme (IMOOP), where we first form the topological graph for online social network and offline connection graph of probability, respectively. Then, the two layers are compressed into a single-layer communication graph. We further prove that the influence maximization in double-layer propagation scheme is NP-hard, then we describe practical greedy heuristics for the resulting NP-hard problems and compute their approximation ratios. Our experiments with real mobility datasets (Brightkite, Gowalla and Foursquare) show that, the proposed propagation scheme achieves a higher information cover ratio, compared with the other propagation methods.

Influence Levels of Users and Meta-Users of a Social Network

This paper considers an extension of the actional model of influence in online social networks. Within the framework of this model, the influence and influence levels of separate agents (users) and meta-agents (subsets of users) are calculated on the basis of their actions taking into account the goals of a control subject (a Principal). We study some properties of the influence function. An example illustrates how the actional model can be used to calculate the influence levels of users in a concrete social network under available initial data.

Analyzing and Detracting Negative Emotion Contagion Influence in Online Social Networks - Position Paper

Analyzing and Detracting Negative Emotion Contagion Influence in Online Social Networks - Position Paper

Credit distribution for influence maximization in online social networks with node features1

Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximum value of influence spread in social networks. How to evaluate the influence is essential to solve the influence maximization problem. Meanwhile, finding out influence propagation paths is one of key factors in the assessment of influence spread. However, since nodes’ degrees are utilized by most of existent models and algorithms to estimate the activation probabilities on edges, node features are always ignored in the evaluation of influence ability for different users. In this paper, besides the node features, the Credit Distribution (CD) model is extended to incorporate the time-critical aspect of influence in online social networks. After assigning credit along with the action propagation paths, we pick up the node which has maximal marginal gain in each iteration to form the seed set. The experiments we performed on real datasets demonstrate that our approach is efficient and reasonable for identifying seed nodes, and the influence spread prediction by our approach is more accurate than that of original method which disregards node features in the influence evaluation and

A Study on Influential User Identification in Online Social Networks

Influential user evaluation is great importance in many application areas of online social networks. In order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model (DRI) to evaluate user influence in online social networks. Influential users can be identified by the influence effect on different distance users based on dynamic regional interaction model. We have applied the influential user identification method to Sina Weibo and the experimental results show that compared with the existing methods the proposed method can identify the influence users in a more accuracy and efficiency way.

Qualities and Inequalities in Online Social Networks through the Lens of the Generalized Friendship Paradox

The friendship paradox is the phenomenon that in social networks, people on average have fewer friends than their friends do. The generalized friendship paradox is an extension to attributes other than the number of friends. The friendship paradox and its generalized version have gathered recent attention due to the information they provide about network structure and local inequalities. In this paper, we propose several measures of nodal qualities which capture different aspects of their activities and influence in online social networks. Using these measures we analyse the prevalence of the generalized friendship paradox over Twitter and we report high levels of prevalence (up to over 90% of nodes). We contend that this prevalence of the friendship paradox and its generalized version arise because of the hierarchical nature of the connections in the network. This hierarchy is nested as opposed to being star-like. We conclude that these paradoxes are collective phenomena not created merely by a minority of well-connected or high-attribute nodes. Moreover, our results show that a large fraction of individuals can experience the generalized friendship paradox even in the absence of a significant correlation between degrees and attributes.

Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks

Demonstrating compelling causal evidence of the existence and strength of peer-to-peer influence has become the holy grail of modern research in online social networks. In these networks, it has been consistently demonstrated that user characteristics and behavior tend to cluster both in space and in time. There are multiple well-known rival mechanisms that compete to be the explanation for this observed clustering. These range from peer influence to homophily to other unobservable external stimuli. These multiple mechanisms lead to similar observational data, yet have vastly different policy implications. In this paper, we present a novel randomized experiment that tests the existence of causal peer influence in the general population—one that did not involve subject recruitment for experimentation—of a particular large-scale online social network. We utilize a unique social feature to exogenously induce adoption of a paid service among a group of randomly selected users, and in the process develop a clean exogenous randomization of treatment and control groups. A variety of nonparametric, semiparametric, and parametric approaches, ranging from resampling-based inference to ego-level random effects to logistic regression to survival models, yield close to identical, statistically and economically significant estimates of peer influence in the general population of a freemium social network. Our estimates show that peer influence causes more than a 60% increase in odds of buying the service due to the influence coming from an adopting friend. In addition, we find that users with a smaller number of friends experience stronger relative increase in the adoption likelihood due to influence from their peers as compared to the users with a larger number of friends. Our nonparametric resampling procedure-based estimates are helpful in situations of networked data that violate independence assumptions. We establish that peer influence is a powerful force in getting users from free to premium levels, a known challenge in freemium communities.This paper was accepted by Sandra Slaughter, information systems.

Models of Influence in Online Social Networks

Online social networks gained their popularity from relationships users can build with each other. These social ties play an important role in asserting users' behaviors in a social network. For example, a user might purchase a product that his friend recently bought. Such phenomenon is called social influence, which is used to study users' behavior when the action of one user can affect the behavior of his neighbors in a social network. Social influence is increasingly investigated nowadays as it can help spreading messages widely, particularly in the context of marketing, to rapidly promote products and services based on social friends' behavior in the network. This wide interest in social influence raises the need to develop models to evaluate the rate of social influence. In this paper, we discuss metrics used to measure influence probabilities. Then, we reveal means to maximize social influence by identifying and using the most influential users in a social network. Along with these contributions, we also survey existing social influence models, and classify them into an original categorization framework. Then, based on our proposed metrics, we show the results of an experimental evaluation to compare the influence power of some of the surveyed salient models used to maximize social influence.