scholarly journals Model-based learning of information diffusion in social media networks

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhecheng Qiang ◽  
Eduardo L. Pasiliao ◽  
Qipeng P. Zheng
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Large Scale ◽  
Viral Marketing ◽  
Propagation Model ◽  
Mixed Integer ◽  
Prediction Errors ◽  
Learning Models ◽  
Approximate Methods ◽  
Social Media Networks

AbstractSocial networks have become widely used platforms for their users to share information. Learning the information diffusion process is essential for successful applications of viral marketing and cyber security in social media networks. This paper proposes two learning models that are aimed at learning person-to-person influence in information diffusion from historical cascades based on the threshold propagation model. The first model is based on the linear threshold propagation model. In addition, by considering multi-step information propagation in one time period, this paper proposes a learning model for multi-step diffusion influence between pairs of users based on the idea of random walk. Mixed integer programs (MIP) have been used to learn these models by minimizing the prediction errors, where decision variables are estimations of the diffusion influence between pairs of users. For large-scale networks, this paper develops approximate methods for those learning models by using artificial neural networks to learn the pairwise influence. Extensive computational experiments using both synthetic data and real data have been conducted to demonstrate the effectiveness of the proposed models and methods.

scholarly journals Influence network design via multi-level optimization considering boundedly rational user behaviours in social media networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Guanxiang Yun ◽  
Qipeng P. Zheng ◽  
Vladimir Boginski ◽  
Eduardo L. Pasiliao
Keyword(s):  
Social Media ◽  
Theoretical Model ◽  
Information Diffusion ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
User Equilibrium ◽  
Propagation Model ◽  
Small Scale ◽  
Social Media Networks ◽  
Game Theoretical Model

AbstractSocial media networks have been playing an increasingly more important role for both socialization and information diffusion. Political campaign can gain more supporters by attracting more mass attention and influencing them directly, while commercial campaigns can increase their companies’ profits by expanding social media connection with new users. To build the optimal network structure to influence the whole, this paper studies mathematical models to simulate the users’ behaviours interacting with others in the information provider’s network. The behaviours of concerns include information re-posting and following/unfollowing other users. Linear threshold propagation model is used to determine the re-posting actions, Boundedly Rational User Equilibrium (BRUE) models are used to determine the following or unfollowing actions. Hence, the topology of the network changes and depends on the information provider’s plan to post various kinds of information. A three-level optimization model is proposed to maximize total number of connections, the goal of the top level. The second level simulates user behaviours under BRUE. The third level maximizes the each user’s utility defined in the second level. This paper solves this problem using exact algorithms for a small-scale synthetic network. For a large-scale problem, this paper uses heuristic algorithms based on large neighbourhood search. This paper also discusses possible reasons why the BRUE model may be a more accurate simulation of users’ actions compared to game theory. Comparisons from the BRUE model to game theoretical model show that the BRUE model performs significantly better than game theoretical model.

scholarly journals A Product Feature Inference Model for Mining Implicit Customer Preferences Within Large Scale Social Media Networks

2015 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Ground Truth ◽  
Inference Model ◽  
Underlying Assumption ◽  
Ground Truth Data ◽  
Social Media Networks ◽  
Customer Preferences ◽  
Product Features ◽  
Online Sources

The acquisition and mining of product feature data from online sources such as customer review websites and large scale social media networks is an emerging area of research. In many existing design methodologies that acquire product feature preferences form online sources, the underlying assumption is that product features expressed by customers are explicitly stated and readily observable to be mined using product feature extraction tools. In many scenarios however, product feature preferences expressed by customers are implicit in nature and do not directly map to engineering design targets. For example, a customer may implicitly state “wow I have to squint to read this on the screen”, when the explicit product feature may be a larger screen. The authors of this work propose an inference model that automatically assigns the most probable explicit product feature desired by a customer, given an implicit preference expressed. The algorithm iteratively refines its inference model by presenting a hypothesis and using ground truth data, determining its statistical validity. A case study involving smartphone product features expressed through Twitter networks is presented to demonstrate the effectiveness of the proposed methodology.

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
User Preferences ◽  
Social Media Data ◽  
Social Media Networks ◽  
Lead User ◽  
Product Features ◽  
Latent Features ◽  
Lead Users ◽  
Media Data

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

scholarly journals Making and Receiving Offers of Help on Social Media Following Disaster Predict Posttraumatic Growth but not Posttraumatic Stress

2020 ◽  
pp. 1-7
Author(s):  
Yael Levaot ◽  
Talya Greene ◽  
Yuval Palgi
Keyword(s):  
Social Media ◽  
Posttraumatic Growth ◽  
Posttraumatic Stress ◽  
Online Social Networks ◽  
Large Scale ◽  
Trauma Exposure ◽  
Virtual Community ◽  
Social Contact ◽  
Convenience Sample ◽  
Social Media Networks

ABSTRACT Objectives: Social media provides an opportunity to engage in social contact and to give and receive help by means of online social networks. Social support following trauma exposure, even in a virtual community, may reduce feelings of helplessness and isolation, and, therefore, reduce posttraumatic stress symptoms (PTS), and increase posttraumatic growth (PTG). The current study aimed to assess whether giving and/or receiving offers of help by means of social media following large community fires predicted PTS and/or PTG. Methods: A convenience sample of 212 adults living in communities that were affected by large-scale community fires in Israel (November 2016) completed questionnaires on giving and receiving offers of help by means of social media within 1 mo of the fire (W1), and the PTSD checklist for DSM-5 (PCL-5) and PTG questionnaire (PTGI-SF), 4 mo after the fire (W2). Results: Regression analyses showed that, after controlling for age, gender, and distance from fire, offering help by means of social media predicted higher PTG (β = 0.22; t = 3.18; P < 0.01), as did receiving offers of help by means of social media (β = 0.18; t = 2.64; P < 0.01). There were no significant associations between giving and/or receiving offers of help and PTS. Conclusions: Connecting people to social media networks may help in promoting posttraumatic growth, although might not impact on posttraumatic symptoms. This is one of the first studies to highlight empirically the advantages of social media in the aftermath of trauma exposure.

USING STARTUP COMMUNICATION FOR OPPORTUNITY RECOGNITION — AN APPROACH TO IDENTIFY FUTURE PRODUCT TRENDS

2016 ◽  
Vol 20 (08) ◽  
pp. 1640016 ◽  
Author(s):  
HAUKE SIMON ◽  
JENS LEKER
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Large Scale ◽  
Early Stage ◽  
Hierarchical Cluster ◽  
Opportunity Recognition ◽  
Social Media Networks ◽  
New Information ◽  
Challenges And Opportunities ◽  
Traditional Approaches

A company’s ability to recognise early-stage opportunities and to understand the dynamics of emerging markets determines the success or failure of new products. Particularly the emergence of new information technology and social media networks provide ample opportunities to leverage a massive amount of data for managerial purposes. However, managers still meet using social media with skepticism and it is not fully understood how to make use of this information for new product development. We introduce a new method on how to use large-scale internet data as a complement to traditional approaches (patent or publication analysis and surveys) to overcome their shortcomings in terms of speed, dynamic and expense to conduct. More specifically, we propose that social media communication of startups can give valuable indications about future product trends especially in rapidly developing fields. Our approach measures the awareness of startups — and their products — as the increase of the communication about the startup on Twitter. Startup communication is a particularly well-suited indicator because startups develop new-to-the-world products or are in the development process. We illustrate our approach by analysing the communication of 545 startups. On a holistic level we determine industry trends. Fintech is among the topics that increase significantly in relevance. We determine more specific categories within the industries by applying cosine-similarity metrics and hierarchical cluster analysis. Subsequently we determine NPD relevant trends by the increase of retweets within these categories. The growing customer awareness of these clusters shows newly evolving customer needs. Incumbents may use this information to adjust to their current portfolio or to find collaboration partners to best meet upcoming challenges and opportunities. We think that the approach can be transferred to a multitude of fields, helping with the analysis of emerging fields and with early stage opportunity recognition.

scholarly journals Systematically Monitoring Social Media: The Case of the German Federal Election 2017

2018 ◽  
Author(s):  
Sebastian Stier ◽  
Arnim Bleier ◽  
Malte Bonart ◽  
Fabian Mörsheim ◽  
Bohlouli ◽  
...  
Keyword(s):  
Social Sciences ◽  
Social Media ◽  
Political Communication ◽  
Content Validity ◽  
Large Scale ◽  
Academic Standards ◽  
Federal Election ◽  
Social Media Networks ◽  
The Social ◽  
The University

It is a considerable task to collect digital trace data at a large scale and at the same time adhere to established academic standards. In the context of political communication, important challenges are (1) defining the social media accounts and posts relevant to the campaign (content validity), (2) operationalizing the venues where relevant social media activity takes place (construct validity), (3) capturing all of the relevant social media activity (reliability), and (4) sharing as much data as possible for reuse and replication (objectivity). This project by GESIS – Leibniz Institute for the Social Sciences and the E-Democracy Program of the University of Koblenz-Landau conducted such an effort. We concentrated on the two social media networks of most political relevance, Facebook and Twitter.

Relative Superiority of Key Centrality Measures for Identifying Influencers on Social Media

2014 ◽  
Vol 10 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Yifeng Zhang ◽  
Xiaoqing Li
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Viral Marketing ◽  
Centrality Measures ◽  
Network Structures ◽  
Agent Based ◽  
Marketing Models ◽  
Practical Implications

Marketers have been increasingly turning to social media for marketing campaigns, including viral marketing. A key step in viral marketing is to identify influencers in order to maximize the reach of a marketing message. Existing research shows that centrality measures, such as degree and betweenness, are effective methods for influencer identification. However, viral marketing models used in different studies vary greatly, making it difficult to compare findings across the studies. In this paper, the authors built an agent-based framework of viral marketing that supports different experiment settings, such as different network structures and information diffusion modes, and used it to study relative superiority of various centrality measures. The results show that relative superiority of the measures are affected by some factors, but not as much by others. Practical implications of the results are discussed.

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