Online Social Networking Activities Research Articles

Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.

Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.

Network analysis of internet addiction and sleep disturbance symptoms

BackgroundInternet addiction (IA) is a behavioral addiction to problematic internet use. IA is associated with poorer sleep quality. Few studies to date, however, have explored the interactions between symptoms of IA and symptoms of sleep disturbance. This study uses network analysis to identify bridge symptoms by analyzing these interactions in a large sample of students. MethodWe recruited 1977 university students to participate in our study. Each student completed the Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI). We used these collected data for network analysis to identify the bridge symptoms in the IAT-PSQI network by calculating the bridge centrality. Furthermore, the closest symptom connected with the bridge symptom was found to identify the comorbidity mechanisms. ResultsThe core symptom of IA and the sleep disturbance network was “I08” (Study efficiency suffers due to internet use). The bridge symptoms between IA and sleep disturbance were “I14” (Surfing the internet late instead of sleeping), “P_DD” (Daytime dysfunction), and “I02” (Spending much time online instead of socializing in real life). Among the symptoms, “I14” had the highest bridge centrality. The edge connecting nodes “I14” and “P_SDu” (Sleep duration) had the strongest weight (0.102) around all the symptoms of sleep disturbance. Nodes “I14” and “I15” (Thinking about online shopping, games, social networking, and other network activities when unable to access the internet) had the strongest weight (0.181), connecting all the symptoms of IA. ConclusionsIA leads to poorer sleep quality, most likely by shortening sleep duration. Preoccupation with and craving the internet while being offline may lead to this situation. Healthy sleep habits should be learned, and craving may be a good point at which to treat the symptoms of IA and sleep disturbance.

Examining various variables related to authentic learning self-efficacy of university students in educational online social networks: Creative self-efficacy, rational experiential thinking, and cognitive flexibility.

The purpose of this study is to examine the relationships between creative self-efficacy (CSE), rational experiential thinking, and cognitive flexibility thinking skills, and authentic learning self-efficacy (ALSE) as a result of authentic learning (AL) activities in educational online social networks. The participants of the research are 102 university students. The study group was determined by a convenient sampling method. Variance-based PLS-SEM using partial least square was used to examine the relationships between research variables. According to the research findings, it is seen that cognitive flexibility in online social networks has a significant effect on experiential ability and rational favorability. Findings show that rational favorability has a positive and significant effect on CSE. Research findings indicate that personal variables such as gender, age, and academic success perception do not have a significant effect on CSE in learning that takes place in online social networks. It was evaluated whether CSE has an effect on online ALSE, and it was found that other sub-dimensions other than “keeping up with technological advancements” are significant.

Evaluation of social bot detection models

Social bots are employed to automatically perform online social network activities; thereby, they can also be utilized in spreading misinformation and malware. Therefore, many researchers have focused on the automatic detection of social bots to reduce their negative impact on society. However, it is challenging to evaluate and compare existing studies due to difficulties and limitations in sharing datasets and models. In this study, we conduct a comparative study and evaluate four different bot detection systems in various settings using 20 different public datasets. We show that high-quality datasets covering various social bots are critical for a reliable evaluation of bot detection methods. In addition, our experiments suggest that Botometer is preferable to others in order to detect social bots.

Predicting cyber offenders and victims and their offense and damage time from routine chat times and online social network activities

Predicting offenders and victims and the timing of their offenses and damages has become a perennial challenge in global cyberspace. This study aimed to solve this challenge through analyses of routine chat times and online social networks. We sampled more than 550,000 online users over 6 months. We also used unsupervised and supervised machine learning to predict cyber offenders and victims and their offense and damage times, respectively. Our predictors, based on routine chat times and online social network inputs, identified future cyber offenders and victims within 2 months. Furthermore, we predicted the hours and days of their offenses and damages within a week. Extraction of routine chat times and online social networks from chat data can help predict future cyber offenders and victims. Our cyber offense and damage time predictor could help prevent future cyber offenses and damages and promote a safer cyberspace for everyone.

Exploring the effect of streamed social media data variations on social network analysis.

To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses. To this end, we developed a systematic comparison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.

A k-Hop Collaborate Game Model: Adaptive Strategy to Maximize Total Revenue

In Online Social Networks (OSNs), interpersonal communication and information sharing are happening all the time, and it is real-time. When a user initiates an activity in OSNs, immediately, he/she will have a certain influence in his/her friendship circle naturally, some users in the initiator's friendship circle will be attracted to participate in this activity. Based on such a fact, we design a k-hop Collaborate Game Model, which means that an activity initiated by a user can only influence those users whose distance are within k-hop from this initiator in OSNs. Besides, we introduce the problem of Revenue Maximization under k-hop Collaborate Game (RMKCG), which identifies a limited number of initiators in order to obtain revenue as much as possible. Collaborate Game Model describes in detail how to quantify revenue and the logic behind it. We do not know how many followers would be generated from an activity in advance, thus, we need to adopt an adaptive strategy, where the decision who is the next potential initiator depends on the results of past decisions. Adaptive RMKCG problem can be considered as a new stochastic optimization problem, and we prove it is NP-hard, adaptive monotone, but not adaptive submodular. But in some special cases, it is adaptive submodular and an adaptive greedy strategy can obtain a $(1-1/e)$-approximation by adaptive submodularity theory. Due to the complexity of our model, it is hard to compute the marginal gain for each candidate user, then, we propose a convenient and efficient computational method. The effectiveness and correctness of our algorithms are validated by heavy simulation on real-world social networks eventually.

Efficiently discovering users connectivity with local information in online social networks

People’s activities in Online Social Networks (OSNs) have generated a massive volume of data to which tremendous attention has been paid in academia and industry. With such data, researchers and third-parties can analyze human beings’ behaviors in social communities and develop more user-friendly services and applications to meet people’s needs. However, often times, they face a big challenge of acquiring the data, as the access to such data is restricted by their collectors (e.g., Facebook and Twitter), due to various reasons, such as their user’s privacy. In this paper, we intend to shed light on leveraging limited local social network topological properties to effectively and efficiently conduct search in OSNs. The problem we focus on is to discover the connectivity of a group of target users in an OSN, particularly from the perspective of a third-party analyst who does not have full access to the network. For the analyst, even discovering a user’s local connections requires issuing a query through OSN APIs (e.g., Facebook Friendlist API or Twitter Followerlist API). We develop searching techniques which demand only a few number of queries for the connectivity discovery.After conducting an intensive set of experiments on both real-world and synthetic data sets, we found that our proposed techniques perform as well as the centralized detection algorithm, which assumes the availability of the entire data set, in terms of the size of the discovered subgraph connecting all target users as well as the number of queries made in the search. The experiment results demonstrate the effectiveness of incorporating topological properties of social networks into searching in the OSNs.

Machine Learning Classifiers for Efficient Spammers Detection in Twitter OSN

Online Social Networking (OSN) is one of the fastest-growing areas of communication over the internet. The popular social networking sites Facebook, LinkedIn, Instagram, Twitter etc. have attracted public helping them connected to family, friends and relatives. People share videos, texts, pictures, opinions, ideas, knowledge and some confidential information knowingly or unknowingly through these sites and thus, OSNs have become the main source of targets for cyber attackers. Cyberattacks have been increasing for the last few decades throwing a serious threat to the internet world. This paper first discusses various OSN threats such as misuse of identity, malware, phishing attacks etc. and also recommends some of the threats preventive measures. Since OSNs activities occur online, it is difficult to trust the users at the other end. Some users misguide genuine users (non-spammers). The one who misguides is known as spammers. Hence, our research work focuses on spammers detection in Twitter using machine learning models. Twitter data is used to carry out this research. For detection, three machine learning models are used, namely, Naive Bayes, Support Vector Machine and Random Forest. Performance of the models is evaluated using Precision, Recall and F measure metrics.

GAMEFEST: Genetic Algorithmic Multi Evaluation measure based FEature Selection Technique for social network spam detection

Social Network sites have become incredibly important in the present day. This popularity attracts the attacker to easily approach a large population and to have access to massive information for performing intrusion activities in Online Social Networks (OSN) including spamming. Spammers not only spread unsolicited messages but also perform malicious activities that harm the user’s financial or personal life and tarnish the reputation of social network platforms. Efficient spam detection requires the selection of relevant features to portray spammer behavior. Most of the existing feature selection techniques use any one of the evaluation measures such as, distance, dependence, consistency, information, and classifier error rate. The feature selection techniques select features from different perspectives based on the evaluation measures. Each evaluation measure produces different subset, and the detection rate differs accordingly. The majority of the existing works focus on the individual feature ranking, and discard the lowest weight feature. Lowest weight feature may produce more accurate prediction if, it is combined with other features. So, there is a need for the feature selection technique that considers the characteristics of all the evaluation measures to produce the appropriate subset, which increases the spam detection rate and assigns a weight for the combination of features. In regard to this, the paper proposes a new multi evaluation measure combined with feature subset selection based on the genetic algorithm, GAMEFEST. The performance of the proposed work has been evaluated using Twitter, Apontador, and YouTube datasets. Experimental results prove that our proposed GAMEFEST with Minimum Surplus Crossover (MSC) improves the efficiency of the learning process and increases the spam detection rate.

SNEFL: Social network explicit fuzzy like dataset and its application for Incel detection

In this paper, with respect to reviewing and comparing existing social networks’ datasets, we introduce SNEFL dataset: the first social network dataset that includes the level of users’ likes (fuzzy like) data in addition to the likes between users. With users’ privacy in mind, the data has been collected from a social network. It includes several additional features including age, gender, marital status, height, weight, educational level and religiosity of the users. We have described its structure, analysed its features and evaluated its advantages in comparison with other social network datasets. On top of that, using unique feature of SNEFL dataset (fuzzy like) for the first time a rule-based algorithm has been developed to detect involuntary celibates (Incels) in social networks. Despite Incels activities in online social networks, until now no study on computer science has been performed to identify them. This study is the first step to address this challenge that society is facing today. Experimental results show that the accuracy of the proposed algorithm in identifying Incels among all social network users is 23.21% and among users who have fuzzy like data is 68.75%. In addition to the Incel detection, SNEFL dataset can be used by researchers in different fields to produce more accurate results. Some study areas that SNEFL dataset can be used in are network analysis, frequent pattern mining, classification and clustering.

A Secure and Sustainable Framework to Mitigate Hazardous Activities in Online Social Networks

Recent years have seen continuous exposure of online social network (OSN) users to the security vulnerability due to socio-technical cyber hazards committed by OSN associates. Unfortunately, the current OSN platforms has lack of functionalities for automatic users protection in initiating online friendship and online users interaction within their circle. Moreover, the users cannot analyze their associates' time-varying and changing behavior, which is a strong indicator of malicious activities. To address this issue, this paper proposes a design of automatic two-phase pre- and post-filtering approach with the objective of mitigating the cyber threats in OSN platforms. In the pre-filtering phase, we propose a method where each user can access a reliable technique to select a friend after getting his/her details authenticity. In the post-filtering phase, a control mechanism is established to recognize malicious posts/actions of the associates in order to limit the spread of the corresponding cyber threats. Our empirical analysis shows some significant comparison data towards hybrid (combined pre- and post-filtering) method, where the users' consents for our proposed method exceed significantly over the other competing techniques. In fact, the proportional mean value of the hybrid method is increased almost twice (2.03) of the existing methods.

Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels. By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration.

Online social network response to studies on antidepressant use in pregnancy

BackgroundAbout 8% of U.S women are prescribed antidepressant medications around the time of pregnancy. Decisions about medication use in pregnancy can be swayed by the opinion of family, friends and online media, sometimes beyond the advice offered by healthcare providers. Exploration of the online social network response to research on antidepressant use in pregnancy could provide insight about how to optimize decision-making in this complex area. MethodsFor all 17 research articles published on the safety of antidepressant use in pregnancy in 2012, we sought to explore online social network activity regarding antidepressant use in pregnancy, via Twitter, in the 48h after a study was published, compared to the social network activity in the same period 1week prior to each article's publication. ResultsOnline social network activity about antidepressants in pregnancy quickly doubled upon study publication. The increased activity was driven by studies demonstrating harm associated with antidepressants, lower-quality studies, and studies where abstracts presented relative versus absolute risks. ImplicationsThese findings support a call for leadership from medical journals to consider how to best incentivize and support a balanced and clear translation of knowledge around antidepressant safety in pregnancy to their readership and the public.

Social bonds and millennial consumers' activity in social networks

This study uses the Relational Bonds Theory to analyse the importance placed on activities in online social networks about variables such relational bonds and number of friends, in a cross-cultural context. We also take into consideration the differences that age can bring in consumers' social media participation behaviour and analyse intra-generational differences within the Millennial group. We use a cross-cultural sample and assess the differences between consumer groups based on cultural characteristics of the country of origin. We showed that consumers who are satisfied with the social value brought to them by their contacts have higher activity rates in social media.

Social bonds and millennial consumers' activity in social networks

This study uses the Relational Bonds Theory to analyse the importance placed on activities in online social networks about variables such relational bonds and number of friends, in a cross-cultural context. We also take into consideration the differences that age can bring in consumers' social media participation behaviour and analyse intra-generational differences within the Millennial group. We use a cross-cultural sample and assess the differences between consumer groups based on cultural characteristics of the country of origin. We showed that consumers who are satisfied with the social value brought to them by their contacts have higher activity rates in social media.

Active on Facebook and Failing at School? Meta-Analytic Findings on the Relationship Between Online Social Networking Activities and Academic Achievement

The popularity of social networking sites (SNSs) among adolescents and young adults has raised concerns that the intensity of using these platforms might be associated with lower academic achievement. The empirical findings on this issue, however, are anything but conclusive. Therefore, we present four random-effects meta-analyses including 59 independent samples (total N = 29,337) on the association between patterns of SNS use and grades. The meta-analyses identified small negative effects of $$ \widehat{\rho} $$ = − .07, 95% CI [− .12, − .02] for general SNS use and $$ \widehat{\rho} $$ = − .10, 95% CI [− .16, − .05] for SNS use related to multitasking. General SNS use was unrelated to the time spent studying for school ( $$ \widehat{\rho} $$ = − .03, 95% CI [− 0.11, 0.06]) and no support for the time displacement hypothesis could be found in a meta-analytical mediation analysis. SNS use for academic purposes exhibited a small positive association, $$ \widehat{\rho} $$ = .08, 95% CI [.02, .14]. Hypotheses with regard to cross-cultural differences were not supported.

A prospective examination of online social network dynamics and smoking cessation.

IntroductionUse of online social networks for smoking cessation has been associated with abstinence. Little is known about the mechanisms through which the formation of social ties in an online network may influence smoking behavior. Using dynamic social network analysis, we investigated how temporal changes of an individual’s number of social network ties are prospectively related to abstinence in an online social network for cessation. In a network where quitting is normative and is the focus of communications among members, we predicted that an increasing number of ties would be positively associated with abstinence.MethodParticipants were N = 2,657 adult smokers recruited to a randomized cessation treatment trial following enrollment on BecomeAnEX.org, a longstanding Internet cessation program with a large and mature online social network. At 3-months post-randomization, 30-day point prevalence abstinence was assessed and website engagement metrics were extracted. The social network was constructed with clickstream data to capture the flow of information among members. Two network centrality metrics were calculated at weekly intervals over 3 months: 1) in-degree, defined as the number of members whose posts a participant read; and 2) out-degree-aware, defined as the number of members who read a participant’s post and commented, which was subsequently viewed by the participant. Three groups of users were identified based on social network engagement patterns: non-users (N = 1,362), passive users (N = 812), and active users (N = 483). Logistic regression modeled 3-month abstinence by group as a function of baseline variables, website utilization, and network centrality metrics.ResultsAbstinence rates varied by group (non-users = 7.7%, passive users = 10.7%, active users = 20.7%). Significant baseline predictors of abstinence were age, nicotine dependence, confidence to quit, and smoking temptations in social situations among passive users (ps < .05); age and confidence to quit among active users. Among centrality metrics, positive associations with abstinence were observed for in-degree increases from Week 2 to Week 12 among passive and active users, and for out-degree-aware increases from Week 2 to Week 12 among active users (ps < .05).ConclusionsThis study is the first to demonstrate that increased tie formation among members of an online social network for smoking cessation is prospectively associated with abstinence. It also highlights the value of using individuals’ activities in online social networks to predict their offline health behaviors.

The web and public confidence in MMR vaccination in Italy.

Measles, mumps and rubella (MMR) vaccination coverage in Italy has been decreasing starting from 2012 and, at the present, none of the Italian regions has achieved the goal of 95% coverage target. A decision of the Court of Justice of Rimini in March 2012 that awarded vaccine-injury compensation for a case of autism has been indicated as a probable trigger event leading to a reduction of vaccine confidence in Italy. The aim of the study was to explore the relationship between MMR vaccination coverage to online search trends and social network activity on the topic "autism and MMR vaccine", during the period 2010-2015. A significant inverse correlation was found between MMR vaccination coverage and Internet search activity, tweets and Facebook posts. New media might have played a role in spreading misinformation. Media monitoring could be useful to assess the level of vaccine hesitancy and to plan and target effective information campaigns.