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.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

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 Emotion shapes the diffusion of moralized content in social networks

2017 ◽  
Vol 114 (28) ◽  
pp. 7313-7318 ◽  
Author(s):  
William J. Brady ◽  
Julian A. Wills ◽  
John T. Jost ◽  
Joshua A. Tucker ◽  
Jay J. Van Bavel
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Political Debate ◽  
Political Issues ◽  
Emotional Words ◽  
Social Media Networks ◽  
Political Ideas ◽  
The Social ◽  
Network Methods

Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call “moral contagion.” Using a large sample of social media communications about three polarizing moral/political issues (n = 563,312), we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks.

scholarly journals Motivations for Virtual Community Participation in Social Commerce: Customers and Sellers in the Malay Lifestyle Blogging Community

2021 ◽  
Author(s):  
◽  
Syahida Hassan
Keyword(s):  
Social Support ◽  
Social Media ◽  
Large Scale ◽  
Virtual Communities ◽  
Virtual Community ◽  
Structured Interview ◽  
Ease Of Use ◽  
Social Commerce ◽  
Commercial Activities ◽  
The Social

<p>Although the field of social commerce has gained a lot of attention recently, there are many areas that still remain unexplored. A new phenomenon emerging within virtual communities is a blurring between social and commercial activities. To date, scholars in the social commerce literature have either focused on customers in the community or on medium to large scale businesses. There has been little research on social commerce communities which include micro-businesses despite their rapid growth in South East Asian countries.  This study explores a social commerce community of Malay lifestyle bloggers, who are a subset of the Malaysian blogosphere community. Bloggers begin by using the personal genre, some then move on to set up online businesses using their personal blogs as a platform. The characteristic of blogging’s ease of use means there are low barriers to starting a small business, merging blogging and commerce. This changes the nature of the community by bringing in a new relationship, as well as relationships between bloggers and readers, there are now also relationships between sellers and customers.  This study aims to understand the motivations for both sellers and customers, and how their relationships as bloggers and readers influence their participation in social commerce within the same community. To address the research objective, 20 sellers and 21 customers who also play a role as bloggers or readers were interviewed. In-depth interviews using laddering and semi-structured interview techniques were carried out to explore social commerce behaviour, the perceived consequences, and goals or values of participation. In addition, observation was also conducted on the platform used by the sellers. Data was coded using NVivo whilst the themes arising from the coding process were transformed into an implication matrix and hierarchical value map using Ladderux software.  This study found that strong ties within the community, influenced by homophily and the sense of virtual community, motivated the customers to participate in commercial activities in order to obtain their goals which included a sense of obligation, loyalty, satisfaction and self-esteem. The relationships influenced customers to trust each other, provide social support and made purchasing products more convenient. Sellers were influenced by the convenience of using social media and the social support provided by the customers which helped them to achieve their goals which are profit and business sustainability.  This study contributes to social commerce theory by highlighting an underexplored type of social commerce setting and addressing how trust can be transferred from social to commercial activities. The findings provide a useful insight for businesses, regardless of their size, to build an understanding of the need to create a good relationship with their customers. For macro-businesses, this model can be used to identify what is lacking in their social media marketing strategy.</p>

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.

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 Social Media Use Predicts Greater Liking in In-Person Initial Interactions

2021 ◽  
pp. 014616722110409
Author(s):  
Jennifer L. Heyman ◽  
Lauren Gazzard Kerr ◽  
Lauren J. Human
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Interactions ◽  
Online Social Networks ◽  
Large Scale ◽  
Media Use ◽  
First Impressions ◽  
Social Media Use ◽  
Large Scale Study ◽  
Interaction Partners

Does how people generally engage with their online social networks relate to offline initial social interactions? Using a large-scale study of first impressions ( N = 806, Ndyad = 4,565), we examined how different indicators of social media use relate to the positivity of dyadic in-person first impressions, from the perspective of the participants and their interaction partners. Many forms of social media use (e.g., Instagram, Snapchat, passive) were associated with liking and being liked by others more, although some forms of use (e.g., Facebook, active) were not associated with liking others or being liked by others. Furthermore, most associations held controlling for extraversion and narcissism. Thus, while some social media use may be generally beneficial for offline social interactions, some may be unrelated, highlighting the idea that how, rather than how much, people use social media can play a role in their offline social interactions.

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.

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