A Multi-Information Spreading Model for One-Time Retweet Information in Complex Networks.

In the realm of online social networks, the spreading of information is influenced by a complex interplay of factors. To explore the dynamics of one-time retweet information spreading, we propose a Susceptible-Infected-Completed (SIC) multi-information spreading model. This model captures how multiple pieces of information interact in online social networks by introducing inhibiting and enhancement factors. The SIC model considers the completed state, where nodes cease to spread a particular piece of information after transmitting it. It also takes into account the impact of past and present information received from neighboring nodes, dynamically calculating the probability of nodes spreading each piece of information at any given moment. To analyze the dynamics of multiple information pieces in various scenarios, such as mutual enhancement, partial competition, complete competition, and coexistence of competition and enhancement, we conduct experiments on BA scale-free networks and the Twitter network. Our findings reveal that competing information decreases the likelihood of its spread while cooperating information amplifies the spreading of mutually beneficial content. Furthermore, the strength of the enhancement factor between different information pieces determines their spread when competition and cooperation coexist. These insights offer a fresh perspective for understanding the patterns of information propagation in multiple contexts.

Predicting the Popularity of Information on Social Platforms without Underlying Network Structure.

The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, traditional methods either rely on complicated time-varying features that are challenging to extract from multilingual and cross-platform content, or on network structures and properties that are often difficult to obtain. To address these issues, we conducted empirical research using data from two well-known social networking platforms, WeChat and Weibo. Our findings suggest that the information-cascading process is best described as an activate-decay dynamic process. Building on these insights, we developed an activate-decay (AD)-based algorithm that can accurately predict the long-term popularity of online content based solely on its early repost amount. We tested our algorithm using data from WeChat and Weibo, demonstrating that we could fit the evolution trend of content propagation and predict the longer-term dynamics of message forwarding from earlier data. We also discovered a close correlation between the peak forwarding amount of information and the total amount of dissemination. Finding the peak of the amount of information dissemination can significantly improve the prediction accuracy of our model. Our method also outperformed existing baseline methods for predicting the popularity of information.

Local-Forest Method for Superspreaders Identification in Online Social Networks.

Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest.

Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users' fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners' learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users' social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful). Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-021-2124-z.

An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders.

- In the ongoing COVID-19 pandemic, people spread various COVID-19-related rumors and hoaxes that negatively influence human civilization through online social networks (OSN). The proposed research addresses the unique and innovative approach to controlling COVID-19 rumors through the power of opinion leaders (OLs) in OSN. The entire process is partitioned into two phases; the first phase describes the novel Reputation-based Opinion Leader Identification (ROLI) algorithm, including a unique voting method to identify the top-T OLs in the OSN. The second phase describes the technique to measure the aggregated polarity score of each posted tweet/post and compute each user's reputation. The empirical reputation is utilized to calculate the user's trust, the post's entropy, and its veracity. If the experimental entropy of the post is lower than the empirical threshold value, the post is likely to be categorized as a rumor. The proposed approach operated on Twitter, Instagram, and Reddit social networks for validation. The ROLI algorithm provides 91% accuracy, 93% precision, 95% recall, and 94% F1-score over other Social Network Analysis (SNA) measures to find OLs in OSN. Moreover, the proposed approach's rumor controlling effectiveness and efficiency is also estimated based on three standard metrics; affected degree, represser degree, and diffuser degree, and obtained 26%, 22%, and 23% improvement, respectively. The concluding outcomes illustrate that the influence of OLs is exceptionally significant in controlling COVID-19 rumors.

Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning.

A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users' quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber's geographical location is extracted to evaluate the signal strength. The regions in which users posted a complaint in OSN are analyzed using a freeware application, which uses the radio base station (RBS) information provided by an open database. Experimental results demonstrated that sentiment analysis based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-recurrent neural network (RNN) with the soft-root-sign (SRS) activation function presented a precision of 97% for weak signal topic classification. Additionally, the results showed that 78.3% of the total number of complaints are related to weak coverage, and 92% of these regions were proved that have coverage problems considering a specific cellular operator. Moreover, a Q-Meter is low cost and easy to integrate into current and next-generation cellular networks, and it will be useful in sensing and monitoring tasks.

Losses and Gains of Psychosocial Resources: Effects on Stress Among Women Undergoing Infertility Treatments and Participating in Social Network Systems.

This theory-driven study assessed the association between infertility-related stress and perceived losses of psycho-social resources; and the buffering effect of resource gains and type of infertility (primary/secondary) on this relationship, among women who participate in online infertility-related social network systems (SNS). Ninety women participating in infertility-related SNS completed online questionnaires assessing resource losses and gains and stress levels. Results: Resource loss significantly predicted stress (ß = .66, p < .001). Resource loss and the number of children were correlated negatively (r = -.22, p < .05). Residency was significantly related to resource loss (r = -.23, p < .05) and perceived stress (r = -.23, p < .05). Israeli participants reported lesser resource loss and lesser perceived stress, compared to participants from other countries. Surprisingly, neither resource gains related to SNS participation nor infertility-type served as moderators in the relationship between resource loss and stress. The association between resource loss and stress supports COR theory formulation of stress etiology. However, while participants noted significant resource gains from use of SNS, these did not buffer the effects of resource loss on stress. Thus, although it may be enticing to turn to SNS for social support, individuals with infertility need to be encouraged to use face-to-face social support too.

Frequencies of Private Mentions and Sharing of Mammography and Breast Cancer Terms on Facebook: A Pilot Study.

BACKGROUND: The most popular social networking site in the United States is Facebook, an online forum where circles of friends create, share, and interact with each other's content in a nonpublic way. OBJECTIVE: Our objectives were to understand (1) the most commonly used terms and phrases relating to breast cancer screening, (2) the most commonly shared website links that other women interacted with, and (3) the most commonly shared website links, by age groups. METHODS: We used a novel proprietary tool from Facebook to analyze all of the more than 1.7 million unique interactions (comments on stories, reshares, and emoji reactions) and stories associated with breast cancer screening keywords that were generated by more than 1.1 million unique female Facebook users over the 1 month between November 15 and December 15, 2016. We report frequency distributions of the most popular shared Web content by age group and keywords. RESULTS: On average, each of 59,000 unique stories during the month was reshared 1.5 times, commented on nearly 8 times, and reacted to more than 20 times by other users. Posted stories were most often authored by women aged 45-54 years. Users shared, reshared, commented on, and reacted to website links predominantly to e-commerce sites (12,200/1.7 million, 36% of all the most popular links), celebrity news (n=8800, 26%), and major advocacy organizations (n=4900, 15%; almost all accounted for by the American Cancer Society breast cancer site). CONCLUSIONS: On Facebook, women shared and reacted to links to commercial and informative websites regarding breast cancer and screening. This information could inform patient outreach regarding breast cancer screening, indirectly through better understanding of key issues, and directly through understanding avenues for paid messaging to women authoring and reacting to content in this space.

Using online social networks to track a pandemic: A systematic review.

BACKGROUND: The popularity and proliferation of online social networks (OSNs) have created massive social interaction among users that generate an extensive amount of data. An OSN offers a unique opportunity for studying and understanding social interaction and communication among far larger populations now more than ever before. Recently, OSNs have received considerable attention as a possible tool to track a pandemic because they can provide an almost real-time surveillance system at a less costly rate than traditional surveillance systems. METHODS: A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment. RESULT: OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics. CONCLUSIONS: OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data.

Exploring the Relationship Between Online Social Network Site Usage and the Impact on Quality of Life for Older and Younger Users: An Interaction Analysis.

BACKGROUND: Analyzing content generated by users of social network sites has been shown to be beneficial across a number of disciplines. Such analysis has revealed the precise behavior of users that details their distinct patterns of engagement. An issue is evident whereby without direct engagement with end users, the reasoning for anomalies can only be the subject of conjecture. Furthermore, the impact of engaging in social network sites on quality of life is an area which has received little attention. Of particular interest is the impact of online social networking on older users, which is a demographic that is specifically vulnerable to social isolation. A review of the literature reveals a lack of knowledge concerning the impact of these technologies on such users and even less is known regarding how this impact varies across different demographics. OBJECTIVE: The objective of our study was to analyze user interactions and to survey the attitudes of social network users directly, capturing data in four key areas: (1) functional usage, (2) behavioral patterns, (3) technology, and (4) quality of life. METHODS: An online survey was constructed, comprising 32 questions. Each question directly related to a research question. Respondents were recruited through a variety of methods including email campaigns, Facebook advertisements, and promotion from related organizations. RESULTS: In total, data was collected from 919 users containing 446 younger and 473 older users. In comparison to younger users, a greater proportion of older users (289/473, 61.1% older vs 218/446, 48.9% younger) (P<.001) stated that Facebook had either a positive or huge impact on their quality of life. Furthermore, a greater percentage of older users strongly agreed that Facebook strengthened their relationship with other people (64/473, 13.5% older vs 40/446, 9.0%younger) (P=.02). In comparison to younger users, a greater proportion of older users had more positive emotions-classified as slightly better or very good-during their engagement with Facebook (186/473, 39.3% older vs 120/446, 26.9% younger) (P<.001). CONCLUSIONS: The results reveal that despite engaging at considerably lower rates with significantly fewer connections, older users gain a greater quality-of-life benefit. Results disclose how both cohorts vary in their use, interactions, and rationale for engaging with Facebook.

Effectiveness of online social networking interventions on social isolation and quality of life of people with psychosis: A systematic review.

BACKGROUND: Social isolation is frequent in people with psychosis, contributing to negative health outcomes. Interventions including online social networking (OSN) may overcome some psychosis-related barriers and facilitate social interactions. However, evidence is currently sparse and needs to be collated in a systematic review to better understand effectiveness. METHOD: Following PRISMA guidelines, this review yielded 9835 results. Eleven publications, reporting data from five RCTs and six non-controlled studies, met the inclusion criteria. Two independent reviewers undertook data extraction and quality assessment, with results narratively synthesised. RESULTS: This review looked broadly at interventions including either purpose-build platforms for peer-to-peer interactions or existing OSN tools. Yet, we only identified interventions utilising purpose-designed platforms. Early small-scale studies suggested OSN interventions reduced social isolation, but larger effectiveness studies did not confirm these effects. No improvements in quality-of-life outcomes were identified. CONCLUSION: Higher quality and longer-term studies did not support effectiveness of current OSN interventions in reducing social isolation or improving quality of life of people with psychosis. These interventions used purpose-built platforms and encouraged OSN between selected individuals, which may explain these outcomes. Future research may explore promoting safe use of mainstream OSN platforms to expand the social networks of individuals with psychosis.

Activities on Facebook reveal the depressive state of users.

BACKGROUND: As online social media have become prominent, much effort has been spent on identifying users with depressive symptoms in order to aim at early diagnosis, treatment, and even prevention by using various online social media. In this paper, we focused on Facebook to discern any correlations between the platform's features and users' depressive symptoms. This work may be helpful in trying to reach and detect large numbers of depressed individuals more easily. OBJECTIVE: Our goal was to develop a Web application and identify depressive symptom-related features from users of Facebook, a popular social networking platform. METHODS: 55 Facebook users (male=40, female=15, mean age 24.43, SD 3.90) were recruited through advertisement fliers distributed to students in a large university in Korea. Using EmotionDiary, the Facebook application we developed, we evaluated depressive symptoms using the Center for Epidemiological Studies-Depression (CES-D) scale. We also provided tips and facts about depression to participants and measured their responses using EmotionDiary. To identify the Facebook features related to depression, correlation analyses were performed between CES-D and participants' responses to tips and facts or Facebook social features. Last, we interviewed depressed participants (CES-D≥25) to assess their depressive symptoms by a psychiatrist. RESULTS: Facebook activities had predictive power in distinguishing depressed and nondepressed individuals. Participants' response to tips and facts, which can be explained by the number of app tips viewed and app points, had a positive correlation (P=.04 for both cases), whereas the number of friends and location tags had a negative correlation with the CES-D scale (P=.08 and P=.045 respectively). Furthermore, in finding group differences in Facebook social activities, app tips viewed and app points resulted in significant differences (P=.01 and P=.03 respectively) between probably depressed and nondepressed individuals. CONCLUSIONS: Our results using EmotionDiary demonstrated that the more depressed one is, the more one will read tips and facts about depression. We also confirmed depressed individuals had significantly fewer interactions with others (eg, decreased number of friends and location tagging). Our app, EmotionDiary, can successfully evaluate depressive symptoms as well as provide useful tips and facts to users. These results open the door for examining Facebook activities to identify depressed individuals. We aim to conduct the experiment in multiple cultures as well.

Detecting rumor outbreaks in online social networks.

Social media platforms are broadly used to exchange information by milliards of people worldwide. Each day people share a lot of their updates and opinions on various types of topics. Moreover, politicians also use it to share their postulates and programs, shops to advertise their products, etc. Social media are so popular nowadays because of critical factors, including quick and accessible Internet communication, always available. These conditions make it easy to spread information from one user to another in close neighborhoods and around the whole social network located on the given platform. Unfortunately, it has recently been increasingly used for malicious purposes, e.g., rumor propagation. In most cases, the process starts from multiple nodes (users). There are numerous papers about detecting the real source with only one initiator. There is a lack of solutions dedicated to problems with multiple sources. Most solutions that meet those criteria need an accurate number of origins to detect them correctly, which is impossible to obtain in real-life usage. This paper analyzes the methods to detect rumor outbreaks in online social networks that can be used as an initial guess for the number of real propagation initiators.

The Role of Personality, Self-Disclosure, and Envy in Maladaptive Social Media Engagement.

Through online social networks (OSNs), individuals establish and maintain social connections to satisfy their need to belong. Recent research suggests that taken too far, one's need to belong can increase envy and lead to maladaptive social media behavior aligned with obsessive-compulsive disorder (OCD). This study examines the role of two personality traits, one's intrinsic need to belong and trait reactance, on feelings of envy and the self-disclosure processes that lead to OCD on social networks. A sample of 354 U.S. adult users of Facebook completed a survey measuring individuals' need to belong, trait reactance, envy, self-disclosure, and OSN-OCD. Regression analyses reveal that need to belong and trait reactance both independently and interactively relate to envy, and that self-disclosure mediates the relationship between envy and OCD on social networks. Those with low trait reactance appear at the lowest risk of OSN-OCD no matter their need to belong. The highest risk profile for online OCD is found in those with both high trait reactance and high need to belong. Overall, our findings support further exploration of one's intrinsic need to belong and trait reactance as personality indicators of risk for OSN-OCD.

Mining and analysing online social networks: Studying the dynamics of digital peer support.

In recent years, the rapid growth of user-generated content has led to much research evaluating the patterns of online information exchange. These studies demonstrate that online communities are valuable data sources which provide rich, longitudinal data that would otherwise be difficult, if not impossible to access. Given the increased research interest, mining and analysing online social networks has become an important research domain, encompassing a variety of approaches. To analyse the large number of observations commonly found in online communities, we propose to first mine the data using a so-called Webscraper and then combine Social Network Analysis (SNA) with Sentiment Analysis to explore both content and relationships. The hands-on approach described in this article is targeted at researchers without a background in technical disciplines. Instead of focusing on some of the specific algorithms that facilitate the mining and analysis of online data, we describe how to use and combine out-of-the-box solutions to collect and analyse the online network data. Moreover, we document the steps taken and present important lessons learnt throughout the process of collecting and analysing data from an online health community with 108,569 registered users who contributed to 197,980 discussions with a total of 484,250 replies. In sum, our method proposes to:•Extract all relevant data from an openly accessible online community using a Webscraper.•Determine and visualise the relationships between users and the properties of the social network as a whole using Social Network Analysis.•Conduct Sentiment Analysis to detect the emotional tone of the online contributions, and to possibly infer further variables from the text such as the personality characteristics of users.

Impact of second-order network motif on online social networks.

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

Understanding social engagements: A comparative analysis of user and text features in Twitter.

Information is spread as individuals engage with other users in the underlying social network. Analysis of social engagements can therefore provide insights to understand the motivation behind how and why users engage with others in different activities. In this study, we aim to understand the driving factors behind four engagement types in Twitter, namely like, reply, retweet, and quote. We extensively analyze a diverse set of features that reflect user behaviors, as well as tweet attributes and semantics by natural language processing, including a deep learning language model, BERT. The performance of these features is assessed in a supervised task of engagement prediction by learning social engagements from over 14 million multilingual tweets. In the light of our experimental results, we find that users would engage with tweets based on text semantics and contents regardless of tweet author, yet popular and trusted authors could be important for reply and quote. Users who actively liked and retweeted in the past are likely to maintain this type of behavior in the future, while this trend is not seen in more complex types of engagements, reply, and quote. Moreover, users do not necessarily follow the behavior of other users with whom they have previously engaged. We further discuss the social insights obtained from the experimental results to understand better user behavior and social engagements in online social networks. Supplementary Information: The online version contains supplementary material available at 10.1007/s13278-022-00872-1.

Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes.

Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the user following network, retweeting network, tweet content and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension of the information diffusion process and may assist in the control of such a process.

Discovering Topic-Oriented Highly Interactive Online Communities.

Community detection is an interesting field of online social networks. Most existing approaches either consider common attributes of social network users or rely on only social connections among the users. However, not enough attention is paid to the degree of interactions among the community members in the retrieved communities, resulting in less interactive community members. This inactivity will create problems for many businesses as they require highly interactive users to efficiently advertise their marketing information. In this paper, we propose a model to detect topic-oriented densely-connected communities in which community members have active interactions among each other. We conduct experiments on a real dataset to demonstrate the effectiveness of our proposed approach.

Temporal Mobility Networks in Online Gaming.

This data-driven study focuses on characterizing and predicting mobility of players between gaming servers in two popular online games, Team Fortress 2 and Counter Strike: Global Offensive. Understanding these patterns of mobility between gaming servers is important for addressing challenges related to scaling popular online platforms, such as server provisioning, traffic redirection in case of server failure, and game promotion. In this study, we build predictive models for the growth and the pace of player mobility between gaming servers. We show that the most influential factors in predicting the pace and growth of migration are related to the number of in-game interactions. Declared friendship relationships in the online social network, on the other hand, have no effect on predicting mobility patterns.