Popular Social Networking Research Articles

MEMORY OF THE GREAT PATRIOTIC WAR IN THE INTERNET SPACE OF RUSSIAN SOCIAL NETWORKS

Being one of the most tragic pages in the history of Russia, World War II left a colossal mark in the memory of the Russian people, combining such conflicting feelings as grief over irreparable losses and the joy of victory. The article is devoted to the most modern forms of preserving the memory of the events of the Great Patriotic War in the modern Russian Internet space. Using statistical data of user activity on the Internet in 2022, the author examines the most popular social networks operating in the Russian Federation and analyzes current trends in the development of modern commemoration practices and ways of forming and preserving collective memory. In particular, the article discusses such Internet platforms as VKontakte and Yandex Zen, where there is significant user activity in maintaining various communities and channels dedicated to the history of the Great Patriotic War, as well as the so-called phenomenon of the mass “flash mob” of Victory Day. The article uses the approaches of the concept of historical memory, which makes it possible to comprehend the trends in the forms and practices of preserving the historical memory of the feat of the Russian people in the space of social networks. The author comes to the conclusion that the growing role and importance of the Internet and social networks in the life of modern society contribute to the fact that in the foreseeable future these resources will become the richest, if not the main, sources of information on the collective historical memory of the Great Patriotic War, as well as space for formation of new commemorative practices.

Factors associated with an increase in alcohol consumption among users of online social networks in Russia in the early months of the COVID-19 pandemic

IntroductionThe various restrictions and physical distancing introduced inRussia in early months of COVID-19pandemic could have a particular impact on people who use alcohol and create new needs for prevention and treatment of associated disorders.ObjectivesThe study was aimed at assessing changes in alcohol consumption among users of online social networks in Russia.MethodsDuring June-September 2020,1518 adult users (18+) of the most popular social networks in Russia (Odnoklassniki, VKontakte,Facebook, Twitter), completed an anonymous online survey regarding alcohol use changes in the first months of COVID-19 pandemic. Binary logistic regression was used to estimate associations of increased alcohol consumption with socio-demographic and pandemic-related factors.Results35.4%of men and 25.6%of women increased their frequency of drinking; 24.9%of men and 17.7%of women increased their typical one-time volume of alcohol consumption,and 28.5% of men and 27.9% of women increased frequency of heavy episodic drinking in the early months of pandemic. After mutual adjustment of independent variables,age18-29 years(OR=1.710;95%CI 1.002-2.917), very strong restrictions of everyday life(3.127; 1.011-9.675)and severe negative consequences in relation to professional or financial situation due to spread ofSARS-CoV-2 (2.247; 1.131-4.465) were positively associated with increase in frequency of drinking. The odds of increase in frequency of heavy episodic drinking were more than twice higher(2.329; 1.001-5.428) among those who experienced severe negative consequences to their professional/financial situation.Higher typical frequency,larger one-time volume of alcohol use,and higher frequency of heavy episodic drinking before pandemic were positively associated with increase of alcohol consumption in early months of pandemic.ConclusionsTimely monitoring of changes in alcohol consumption during pandemic can allow prevention of alcohol-related disorders, including mental disorders, among users of online social networks.DisclosureNo significant relationships.

Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious handling by various bots. The malicious handling phenomenon expands in online discourse, especially during the electoral periods, where except the legitimate bots used for dissemination and communication purposes, the goal is to manipulate the public opinion and the electorate towards a certain direction, specific ideology, or political party. This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. To this end, a supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm, where the hyper-parameters are tuned via cross-validation. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions by calculating feature importance, using the game theoretic-based Shapley values. Experimental evaluation on distinct Twitter datasets demonstrate the superiority of our approach, in terms of bot detection accuracy, when compared against a recent state-of-the-art Twitter bot detection method.

Development of a content-plan for promoting hotels in a social network

The article is devoted to the promotion of hotel enterprises in social networks. The author highlights the methods of creating a content plan by analyzing the target audience of the hotel. The author focuses on the fact that for high brand awareness it is important to learn how to correctly position the enterprise and properly promote it. Also in this article will be considered the main ways of promotion through popular social networks and the benefits of creating a personal hotel brand.

Transformation of the frame of the «picturesque Ukrainian village» as a visual representation of some features of national character in the late ХІХ – early XXI century

The purpose of the article is to compare some visual frames, produced by narrative ideas about the Ukrainian national character in the XIX-XX centuries, with visual representations of these ideas in popular Ukrainian social media and networks in the first quarter of the XXI cent. The research methodology combines quantitative (content analysis, statistical calculations) and qualitative (structural semiotic-semantic analysis) methods with culturological interpretation of the results. The scientific novelty lies in the establishment of external and internal changes in the visual representations of the Ukrainian national character in the socio-cultural context of «secondary» modernization. Conclusions. The frame of the «picturesque Ukrainian village» as a visual representation of the leading features of the Ukrainian national character — «emotionalism and sentimentalism, sensitivity and lyricism» and partly «individualism» (D. Chizhevsky), has been created in the fine arts of the late ХІХ cent. In Ukraine at the beginning of the XXI century in the popular social media and networks, this frame has changed only insignificant internally (changing the perspective of the image, increasing the scale of the image, expanding the landscape component). Instead, its external transformation into a global media product was taking place. This transformation indicates the incompleteness of the modernization processes in Ukraine, the conservation of a significant pre-modern component in its culture.
 Key words: "picturesque Ukrainian village", national character, narratives, visual representations, fine arts, socialmedia

The Communication in Social Media About COVID-19 Vaccine: Mapping and Bibliometrics Analysis

COVID-19 emerged at the end of 2019 in China, and has spread to all corners of the world. So many parties are creating vaccines to end the COVID-19 pandemic. Simultaneously in today's digital world, there are many studies discussing the COVID-19 vaccine spread in terms of communication on social media which is the most popular social network today. Therefore, the purpose of this study is to explain global research trends related to the use of social media as a means of spreading COVID-19 vaccines, both positive and negative, from 2020 to 2021. This study uses bibliometric analysis and also uses VOSViewer to map scientific publications. This study also uses the Word frequency feature in NVIVO 12 plus. Findings in this study that the publication of scientific research on communication in social media about the COVID-19 vaccine has increased in the last two years. The mapping covers all countries whose publications are indexed by Scopus, the United States has the most significant contribution to research publications. Journal of Medical Internet Research is the journal that publishes the most research. The University of Pennsylvania is the institution that has the highest contribution. Topics that have good opportunities for future research are interpersonal, vaccination, vaccines, immunology, and COVID-19 vaccines. The narrative of global scientific research publications related to communication in social media about the COVID-19 vaccine is COVID-19, vaccine, and social media.

The Interface between the Brand of Higher Education and the Influencing Factors

The world is changing faster than ever before. Continuous changes are also affecting the higher education sector. The number of programs on offer is growing, attraction strategies are changing, and the branding and positioning of higher education institutions are gaining significance. The growing influence of social networks on personal choice cannot be ignored. Of the world’s population, 57% uses social networks and spends an average of 2.5 h a day on them. The most popular social network, Facebook, has up to 2.9 billion active users every month. Therefore, the questions arise as to which factors influence one or another consumer choice, how social networks contribute to brand awareness, and what impact brand has on the higher education sector. After systematic and comparative analysis of concepts published in the scientific literature, the analysis of brand, brand promotion concepts, and factors that increase brand awareness is performed. This study seeks to determine whether and to what extent individual factors influencing student motivation and social networking influence the distinctiveness of a higher education institution brand and how factors influencing student motivation and social networking affect the distinctiveness of higher education institutions’ brands in general. The results of this study can help higher education institutions to develop their own plans, strategies, and good practices. Research methods: systematic and comparative analysis of concepts and methods published in the scientific literature, mathematical and statistical methods, statistical processing, and expert survey.

Problematic social media use and associated consequences on academic performance decrement during Covid-19

Based on the Stressor-Strain-Outcome (SSO) model, this study investigates the mechanism by which problematic social media use (PSMU) influences academic performance decrement via two psychological factors namely, technostress and exhaustion. It also identifies the social media usage characteristics of undergraduate students in Saudi Arabia during Covid-19. An online questionnaire was distributed to undergraduate social media users from Shaqra University. Analysis of the 312 responses shows that PSMU is a significant stressor affecting both technostress and exhaustion and thus reducing academic performance. Moreover, both are important mediators in the link between PSMU and decline in academic performance. WhatsApp, Snapchat, Twitter and Instagram are the most popular social media networks used by the undergraduates while Facebook and Skype are the least preferred. Roughly one-third (31.41%) of the students spend more than six hours a day on social media. The findings will help educators and other stakeholders to design appropriate interventions and strategies to minimize the unfavourable effects of social media in academia.

LOCATION PREDICTION ON TWITTER USING MACHINE LEARNING TECHNIQUES

These days, location prediction of users using online social media generates a lot of research. For decades, researchers have looked on automatic location recognition in relation to or referenced in documents. As one of the most popular online social networking sites, Twitter has attracted a large number of users who send millions of tweets on a regular basis. Because of the global reach of its users and the constant flow of messages, location prediction on Twitter has gotten a lot of attention lately. Tweets, brief, noisy, and rich-natured communications, provide numerous study hurdles to researchers. A general picture of location prediction using tweets is investigated in the suggested framework. Tweet location, in particular, is anticipated based on tweet content. It is very important to outline tweet content and situations highlighted how the difficulties are dependent on certain text inputs. Here, we apply machine learning techniques like as naive bayes, Support Vector Machines, and Decision Trees to estimate the user's location from tweet content.

Visual Sentiment Analysis from Disaster Images in Social Media

The increasing popularity of social networks and users’ tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people’s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

An Assessment of Social Networking Sites Addiction Among Female Undergraduate Students in Kwara State, Nigeria

Online access of information and other purposes through social networking sites are becoming more easier day by day due to increased penetration of internet and affordability ubiquitous nature of computer devices and platforms. What is the pattern and trend of social networking sites addiction (SNA) among female undergraduate students. This study employs descriptive research design using survey method. Sampling was by simple random among female undergraduate students in two selected universities in Kwara state of Nigeria. This study engages four objectives to guide the research. Findings revealed the most popular social networking sites, factors responsible for SN usage, different causes for SN addiction and solutions to SN addiction. There was no significant relationship between age and SN addiction, while there is a strong positive significant relationship between course of study and SNA and a positive significant relationship between institutions and factors affecting SNA. It was concluded that certain factors and causes were responsible SNA among female undergraduate students. Recommendations on how to avoid SNA were provided.

Examining stigma relating to substance use and contextual factors in social media discussions

BackgroundStigma associated with substance use can have severe negative consequences for physical and mental health and serve as a barrier to treatment. Yet, research on stigma processes and stigma reduction interventions is limited. AimWe use a social media dataset to examine: 1) the nature of stigma-related experience related to substance use; and 2) salient affective and temporal factors in the use of three substances: alcohol, cannabis, and opioids. MethodsWe harvested several years of data pertaining to three substances – alcohol, cannabis, and opioids – from Reddit, a popular social networking platform. For Part I, we selected posts based on stigma-related keywords, performed content analysis, and rendered word clouds to examine the nature of stigma associated with these substances. In Part II, we employed natural language processing in conjunction with hierarchical clustering and visualization to explore temporal and affective factors. ResultsIn Part I, internalized stigma was most commonly exhibited. Anticipated and enacted stigma were less common in posts relating to cannabis compared to the other two substances. Work, home, and school were important contexts in which stigma was observed. Part II showed that temporal markers were prominent; post authors shared stories of substance use journeys, and timelines of their experience with quitting and withdrawals. Shame, sadness, anxiety, and fear were common, with shame being more prominent in alcohol-related posts. ConclusionOur findings highlight the importance of contextual factors in substance use recovery and stigma reduction and offer directions for future interventions.

Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users

PurposeThe intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.Design/methodology/approachWe propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.FindingsUsing the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.Originality/valueThis paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

A qualitative exploration into personal psychological agency in Instagram use

Instagram is an increasingly popular photography-based social networking platform. Recent studies that seek to understand the impact of engaging with Instagram on the behaviour and wellbeing of users have generated surprisingly conflicting evidence. We argue that in much of the existing research, “using Instagram” is considered a standardised, homogenous, experience, much like watching a TV show or film. The reality is that Instagram supports a wide range of possible experiences for users, who have some agency in shaping that experience. The aim of this study is to better understand the various ways in which Instagram users actively participate, or not, in the shaping and curation of their own Instagram experience. There were 17 participants in this study, 10 females and seven males, with an age range of 18–39 years old, who were recruited through a university mailing list. Semi-structured interviews were conducted and a combination of inductive and deductive thematic analysis was carried out to identify and organise patterns of meaning in the data. Specifically, open-coded interview transcripts were deductively analysed, following Bandura's (2001) framework for classifying the experience of agency, using the organising concepts of; intentionality, forethought, self-reactiveness, and reflection. Our findings, firstly, describe a unique set of aims, motivations, and strategies that participants employed to shape and curate a personal Instagram experience. Secondly, findings suggest that Bandura's framework provides some explanation for the variation in experience reported by our participants. Specifically, while some participants were active agents in their use of Instagram, others were less capable of commanding their experience. Thirdly, agentic practices were not necessarily stable; some participants were active in some areas of Instagram use and passive in others. Their engagement changed with context and mood, and their manner of Instagram use developed considerably over time. Finally, there were observable differences between usage patterns and agentic practices between the older and the younger participants. Our findings indicate that Instagram use is idiosyncratic, complex, and changeable, providing some explanation for why previous studies on the impact of Instagram on wellbeing have often been contradictory.

US Black Maternal Health Advocacy Topics and Trends on Twitter: Temporal Infoveillance Study

BackgroundBlack women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts.ObjectiveThis study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic.MethodsTweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets.ResultsA variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73%, and discussions about creating space for Black women decreased by just under 30%.ConclusionsThe results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts.

Online Information Search Behaviour During Pandemic Covid – 19 among Young Travellers in Malaysia

During the Coronavirus illness (COVID-19) epidemic, young generations became accustomed to searching for travel and tourism information on the internet. As a result of the pandemic's limits on movement, Internet usage has risen dramatically. The developments in online information search related to travel and tourism must be investigated in order to aid the tourist industry's sustainability. During the Movement Control Order (MCO) period, this study looked at how young Malaysians searched for travel and tourism-related information online. The descriptive study was used to evaluate four variables: prior travel experience, online information participation, content, and travelling needs. An online poll was used to gather information from Malaysians across the country's 14 states. The replies of 348 people were analysed to see how the younger generation in Malaysia uses the internet to find information. According to the findings, nearly 90% of those surveyed have travelled at least once. Trivago.com is the most popular travel site, and Facebook is the most popular social networking platform, according to the statistics. In terms of content, the majority of searches are for travel destinations, although the majority of travellers' needs are for domestic travel. The findings of this research can be used to design marketing tactics for tourism products as well as recovery measures for the travel and tourism industry. This research can also add to our understanding of Malaysia's information search systems. This will assist Malaysia's tourism industry in making more informed decisions.

“From All Sides”: Black-Asian Reddit Communities Identify and Expand Experiences of the Multiracial Microaggression Taxonomy

The purpose of this paper is to examine the identity narratives of online Black-Asian communities concerning their multiracial microaggression experiences. As we are investigating an under-explored population, we utilize a purposive sampling method to narrow forum engagement by Black-Asian users on the popular social media network, Reddit. Our research is guided by multiracial critical theory (MultiCrit) which highlights monoracism as a ubiquitous element of the monoracial paradigm of race. In our research, we employ a content analysis using Black-Asian microaggression narratives to test the existing multiracial microaggression taxonomy. Our findings demonstrate that Black-Asian subreddit communities identified all of the existing multiracial microaggression categories: (1) exclusion or isolation; (2) exotification and objectification; (3) assumption of monoracial or mistaken identity; (4) denial of multiraciality; (5) pathologizing of identity and experiences; and (6) microaggressions based on racial stereotypes. Our analysis also found an emerging microaggression category entitled (7) white-mixed superiority. These multiracial microaggressions drew on monoracist ideologies and the monoracial paradigm of race, which exclude Black-Asian people and aid the maintenance of white supremacy in the United States. As such, we suggest further inquiry and expansion of the multiracial microaggression taxonomy to include how racism, particularly the monoracial paradigm of race, influences non-white multiracial identities.

RUSSIANS OF KAZAKHSTAN AND THEIR NATIONALITY: HOW ARE CIVIC CONSCIOUSNESS AND SOCIAL NETWORKS CONNECTED?

The Russian-Ukrainian conflict has clearly demonstrated that the construction of national and ethnic identity in the post-Soviet space is still in an active phase. At the same time, the rapid development of mobile technologies and the huge popularity of social networks raise the question of the relationship between the use of social networks and a sense of nationality. Based on 22 semi-structured interviews with young Russians in Kazakhstan, my research describes the role of social media in the process of ethnonational and citizenship of Russians who: 1) were born and raised in independent Kazakhstan and 2) are currently active users of social networks. Research has shown that the use of social networks can help in maintaining multiple identities and in rethinking prescribed identity. Moreover, young ethnic Russians support the multicultural civic paradigm of Kazakhstan and form an appropriate information environment in personal social networks. Looking at news feeds in VKontakte, Facebook and Instagram the younger generation of Russians in Kazakhstan forms a certain idea of the country of their birth and residence, which allows them to reconcile their multiple national identities and avoid social uncertainty.

Instagram-Assisted Learning of Collocations and Colligations: The Case of EFL Learners

Instagram, as a popular social networking platform, allows users to share photos and videos with other users. This research investigated the effect of Instagram-assisted instruction on the learning of collocations and colligations1 by Iranian EFL learners. Fifteen participants who were selected based on a language proficiency test took part in this research. The participants were first pre-tested on their collocational and colligational knowledge. The collocational and colligational items that were answered incorrectly by 50 percent or higher were selected for treatment. Afterwards, the researchers prepared nine videos, containing 41 collocational and colligational items, and posted them on Instagram during a five- week instruction period. The participants were asked to watch the videos, and carry out the related exercises in the caption section of the post, through the usage of the comments sections on Instagram. After posting all the videos, the pre-test was run again as a post-test. The results indicated a significant difference between the mean scores of the pre- and post-test, highlighting the fact that the treatment had been effective. In addition, semi-structured interviews were conducted with 10 participants in order to find out their attitudes towards language learning via Instagram. The learners indicated a positive attitude towards using Instagram for their language learning purposes. In conclusion, the research showed that Instagram-assisted instruction, when properly implemented and designed, can be beneficial to the development of second language learning.