Growth Of Social Networking Sites Research Articles

An Efficient Fake News Detection using Machine Learning

Fake news and hoaxes have been there since before the advent of the Internet. The widely accepted definition of Internet fake news is: fictitious articles deliberately fabricated to deceive readers”. Social media and news outlets publish fake news to increase readership or as part of psychological warfare. In general, the goal is profiting through clickbaits. Clickbaits lure users and entice curiosity with flashy headlines or designs to click links to increase advertisements revenues. This exposition analyzes the prevalence of fake news in light of the advances in communication made possible by the emergence of social networking sites. The purpose of the work is to come up with a solution that can be utilized by users to detect and filter out sites containing false and misleading information. In this paper we have used simple and carefully selected features of the title and post to accurately identify fake posts. The experimental result shows maximum of 87.04% accuracy for logistic model.

‘Why should we turn to fascists in their own language?’ Affordances and constraints of networked counterpublics as experienced by the group members

ABSTRACT The emergence of social networking sites (SNSs) and new media has led to scholarly interest in the possibilities they offer for creating counter publics - discursive arenas where subordinated groups invent and circulate counterdiscourses in opposition to the dominant ideology. However, most empirical research has focused on describing discursive strategies used in counterpublics and analyzing networks of specific movements or organizations, while the perception and experiences of group members remain largely unexplored. To fill this research gap, this study analyzes the internal negotiations surrounding the Facebook page 'Write it down! I am an Arab,' which serves as a counterpublic for Arab citizens of Israel. The study employs two methods: (1) interviews with 14 participants of the 'Write it down! I am an Arab' Facebook page, and (2) a thematic analysis of 1,529 comments made by Arab respondents on the same page. The findings reveal a complex understanding of the counter public, defined by internal contradictions among group members concerning its goals and pragmatics, and especially its orientation or position. While the page participants view it as a subversive platform and an alternative means to access the dominant public sphere, many commenters consider the act of addressing the Jewish public as subservient, perpetuating and normalizing prevailing power dynamics. These findings highlight that counterpublics are not homogeneous structures automatically granted natural legitimacy by the group members, and that the counterpublic's position or orientation is a crucial factor in the discursive negotiations taking place within the group.

The Impact of Social Media Relationships on e-WOM in Syria and Hungary

Cultural values play a crucial role in the formation of individuals’ behaviour. With the emergence of social networking sites, which have formed a parallel world to the real world, the behaviour of individuals and their motives to engage in electronic word of mouth (eWOM) on social media platforms has become extremely important for brands. Due to a relative lack in studies focusing on the cultural difference-based impact of social relationships on eWOM in social media, the present study seeks to address this gap as its broad objective is to investigate the effect of social relationship variables on eWOM in social media by comparing two different cultures, namely Syria and Hungary in order to explore whether social relationships exert a more significant impact on eWOM in a collectivistic society than in an individualistic one. An explanatory research design was adopted and the data was collected by means of a questionnaire survey. The final sample included 113 Syrian and 90 Hungarian respondents, all of them aged 35 or below. It was found that tie strength among young adults has a more positive impact on eWOM in Syria than in Hungary, while no homophily impact was found on eWOM in either. Regarding the other social relationship variables examined in the study (trust, normative and informational influences), the results showed that they do not have a more positive impact on eWOM in Syria than in Hungary. The research is believed to have contributed to previous investigations on the impact of social relationships on eWOM via social media by providing insights into the role of a cultural value dimension in determining the extent to which individuals are affected by their cultural background, whether collectivistic or individualistic, when they interact in social media platforms. At the same time, it is acknowledged that the study has limitations thus future examinations are necessary.

Antecedents and consequences of interpersonal electronic surveillance in romantic relationships

ABSTRACT The proliferation of communication technologies and the growth of social networking sites provide new avenues for electronic surveillance of romantic relationship partners. This study evaluated attachment dimensions and romantic jealousy as complementary explanations for interpersonal electronic surveillance in romantic relationships. In addition, this study investigated the moderating effect of dyadic trust on the link between interpersonal electronic surveillance and relationship satisfaction. Three hundred and twenty-one individuals currently involved in romantic relationships with active social media accounts completed measures assessing attachment anxiety and avoidance; cognitive and emotional jealousy; interpersonal electronic surveillance; dyadic trust; and relationship satisfaction. Results indicated partial support for a statistical model in which jealousy mediated the associations between attachment and interpersonal electronic surveillance. In addition, findings supported the moderating role of dyadic trust on the association between interpersonal electronic surveillance and relationship satisfaction.

The Limits of Social Media Mobilization: How Protest Movements Adapt to Social Media Logic

The emergence of social networking sites offers protest movements new ways to mobilize for action and draw attention to their issues. However, relying on social media also creates challenges, as social media follow their own principles. If protest movements want to be visible in news feeds, they have to adapt to so-called social media logic, as originally postulated in mediatization research. The principles of social media have been conceptualized. However, there is a lack of empirical research on how political actors perceive and orient to this logic, how they learn about it, and the consequences for mobilization (i.e., communicating protest issues as well as taking protest action). As protest movements are an integral part of modern democracies, use social media somewhat intensively, and usually build on a fluid network structure that allows us to examine adaptation processes in greater detail, they are particularly suitable for addressing these questions. Semi-structured interviews with activists organizing protest actions or managing social media accounts from 29 movement organizations in Germany (<em>N</em> = 33) revealed that protest movements have internalized social media logic and paid attention to not only the design but also the timing of posts to suit algorithms. The protest organizations generally built on their experience with social media. The degree to which they followed these principles was based on available resources. Limits of this adaptation arose, for example, if sensitive or negative content rarely produced likes or, increasingly, personalization evoked a presumed hierarchy within the movements.

A machine learning approach for Urdu text sentiment analysis

Product evaluations, ratings, and other sorts of online expressions have risen in popularity as a result of the emergence of social networking sites and blogs. Sentiment analysis has emerged as a new area of study for computational linguists as a result of this rapidly expanding data set. From around a decade ago, this has been a topic of discussion for English speakers. However, the scientific community completely ignores other important languages, such as Urdu. Morphologically, Urdu is one of the most complex languages in the world. For this reason, a variety of unique characteristics, such as the language's unusual morphology and unrestricted word order, make the Urdu language processing a difficult challenge to solve. This research provides a new framework for the categorization of Urdu language sentiments. The main contributions of the research are to show how important this multidimensional research problem is as well as its technical parts, such as the parsing algorithm, corpus, lexicon, etc. A new approach for Urdu text sentiment analysis including data gathering, pre-processing, feature extraction, feature vector formation, and finally, sentiment classification has been designed to deal with Urdu language sentiments. The result and discussion section provides a comprehensive comparison of the proposed work with the standard baseline method in terms of precision, recall, f-measure, and accuracy of three different types of datasets. In the overall comparison of the models, the proposed work shows an encouraging achievement in terms of accuracy and other metrics. Last but not least, this section also provides the featured trend and possible direction of the current work.

Effectiveness of Regulatory Policies on Online/Digital/Internet-Mediated Alcohol Marketing: a Systematic Review

BackgroundThe rapid growth of social networking sites and video sharing platforms has created an opportunity for the alcohol industry to employ advanced advertising and marketing approaches to target their audiences, increasingly blurring the lines between commercial marketing and user-generated content, which poses a challenge for effective regulation.MethodsWe conducted a systematic search through three peer-reviewed journal databases (WoS, PubMed, Scopus). Studies were included if published in English, after 2004, and assessed statutory regulation or voluntary industry codes, enacted by an EU or nation’s governmental agency or private entity, and with the intent to restrict digital alcohol advertising. In addition, we conducted a manual search of gray literature.ResultsA total of 4690 records were identified. After duplicate removal and full-text assessment, 14 articles were examined. Our findings indicate that children and adolescents may often be exposed to alcohol advertisements on social media and websites due to industry’s self-regulatory age-affirmation systems being largely ineffective at preventing under-aged access. Cases of self-regulatory violations by the alcohol industry, and increasingly innovative ‘gray-area’ advertising approaches have also been noted. Additionally, research illustrates a lack of developed statutory restrictions of digital alcohol advertising and instead continued reliance on voluntary industry self-regulation.ConclusionsThere is a substantial need for further research to examine the effectiveness of digital alcohol advertising restrictions in social media, websites and image/video sharing platforms. Moreover, there is a necessity for countries to develop comprehensive statutory frameworks, which would effectively restrict and monitor rapidly advancing digital alcohol advertising practices on new digital media.

Factors affecting rumor spreading of social network sites

The emergence of social network sites (e.g. Facebook, Twitter, and YouTube etc.) has been playing an important role for modern people’s lives. The social network sites provide users online maintaining and expanding interpersonal space and opportunities. Many social network sites have been a platform to share the news and information with friends and family. The social network sites facilitate the information flow through user’s friend circle, so various information spreads quickly and so does rumor. This study adopts the theoretical perspective of an Elaboration Likelihood Model to identify the informative and normative factors that influence the credibility judgment of online rumors in social network sites context. Based on the 335 valid samples from the online questionnaire, statistical testing of research models using partial least squares. The results of this study are similar to the ELM claim that argument quality and source credibility have significant effects on rumor credibility. This indicates that rumor spreading of SNSs is based not only on the argument quality, but also on the source credibility. Moreover, compared with argument quality, the source credibility has a stronger impact on rumor credibility. This means that the degree to which the user needs source credibility plays a major key role to decide the rumor credibility. The study also suggests the possible importance of argument quality and source credibility of rumor. Specifically, we identified three components of argument quality and four components of source credibility.

HNTSumm: Hybrid text summarization of transliterated news articles

Data generated from social networking sites, blogs, digital magazines, and news websites is the largest human-generated data. Summarization is the process of extracting the crux of a document which when done manually can be tedious and deluging. Automatic text summarization is an approach that encapsulates long documents into a few sentences or words by enwrapping the gist and the principal information of the document. With the growth of social networking sites, eBooks, and e-Papers, the prevalence of transliterated words in text corpora is also on the rise. In this paper, we propose a word embeddings-based algorithm called HNTSumm by combining the advantages of unsupervised and supervised learning methods. The proposed algorithm HNTSumm algorithm is an imminent method for automatic text summarization of huge volumes of data that can learn word embeddings for words transliterated from other languages to English by utilizing weighted word embeddings from a Neural Embedding Model. Further, the amalgamation of extractive and abstractive approaches yields a concise and unambiguous summary of the text documents as the extractive approach eliminates redundant information. We employ a hybrid version of the Sequence-to-sequence models to generate an abstractive summary for the transliterated words. The feasibility of this algorithm was evaluated using two different news summary datasets and the accuracy scores were computed with the ROUGE evaluation metric. Experimental results corroborate the higher performance of the proposed algorithm and show HNTSumm outperforms relevant state-of-the-art algorithms for datasets with transliterated words.

MOTIVASI MEMBAGIKAN KONTEN TANAMAN HIAS DI SOCIAL NETWORKING SITES

Covid-19 is a pandemic that acts as a catalyst for drastic lifestyle change for people around the world. Measures to limit the spread of the disease causes majority of Indonesians to work and study at home. This spurs the growth of social networking sites (SNS) usage. Stay-at-home order also gives rise to the popularity of ornamental plant collecting. The combination of the two activities is evident in the phenomena of posting contents about ornamental plant collection in SNS. According to uses gratification perspective, new communication technologies and the subsequent activities they facilitate function to satisfy the needs of the audiences. This research aims to explore the motivation typologies that are related to posting ornamental plant contents on SNS. Qualitative data was collected from 5 informants based on grounded theory procedures. Through semi-structured interview, three types of typologies were found. The first is classic typology that includes entertainment, pastime, identity management, social interaction and knowledge sharing. Second is expanded typology; social influencing and economic motivation. The third is emergent typology that includes sharing positivity. This article serves as a preliminary research that delves into communication phenomena during the Covid-19 pandemic. It is recommended that future research validate the typologies found by performing quantitative studies.

Ranking of advertising goals on social network sites by Pythagorean fuzzy hierarchical decision making: Facebook

The rapid growth of social networking sites (SNSs), known as web-based services, has become a natural digital phenomenon, reinforcing e-commerce concepts such as business-to-consumer (B2C) and consumer-to-consumer (C2C), known as social commerce. One platform that provides end-to-end connections, from increasing brand awareness to creating new opportunities in the contemporary marketing approach, is Facebook, which reaches a broad audience of advertisers with the advantage of many users. Facebook has updated its ad targets in recent years. Choosing the right advertising target will directly affect company and campaign performance. Considering this problem structure, it is compatible with fuzzy set theory since it contains conflicting criteria and high complexity in an environment of uncertainty. As far as it is known from the literature, although there are many academic studies on Facebook ad analysis, there is no study investigating advertising targeting with fuzzy set theory. Therefore, in the decision model developed for Facebook advertising targeting, a fuzzy decision model is proposed for targeting by the campaign structure. The first stage of the model includes the combined use of AHP-TOPSIS methods by evaluating performance criteria and presenting advertising targets in social media campaigns with Pythagorean fuzzy sets. As a result, it has been reached that the awareness-raising strategy for local or global users should include campaign activities that will increase brand awareness.

5G Network in Content Based Emotion Detection by Sentimental Analysis Integrated with Opinion Mining and Deep Learning Architectures

The rapid growth of social networking sites in the Internet era has made them a necessary tool for sharing emotions with the entire world. To extract emotions from text, a variety of tools and approaches are available in fields of opinion mining as well as sentiment analysis. These researches propose novel technique opinion mining based emotion detection from the input social content using deep learning architectures. Here the input has been obtained as social media content based on opinion miningby 5G networks. The input has been processed for noise removal, smoothening and normalization. This processed input has been segmented using Markov model based convolutional neural networks (MMCNN). The segmented data has been classified using Canonical Correlation AnalysisBayesian neural network.An opinion mining method that analyzes statements regarding computer programming and predicts or recognizes their polarity was implemented, along with an earlier module that was integrated into an intelligent learning environment. These three steps made up the creation of the module. We assessed the corpus, text polarity precision, and emotion recognition. Experimental analysis has been carried out for various social media content collected by opinion mining in terms of accuracy, precision, recall, F-1 score, AUC.Proposed technique attained accuracy of 99%, precision of 96%, recall of 96%, F-1 score of 95%, AUC of 89%.

SISTEM INFORMASI JEJARING SOSIAL DAN FORUM DISKUSI BERBASIS PHP

The rapid advancement of technology in the field of information has encouraged the emergence of social networking sites. Sites that have many interesting features and can be easily accessed and applied by all circles of society. Distance becomes meaningless, differences become the reason for unity for every participating person, including a discussion forum site that is useful for showing the existence of the community or organization. Currently the BALMON office has a very wide range to support things that are not possible to carry out. Maintenance and coordination meetings make it impossible to do every day, therefore BALMON requires a social networking facility so php social networking system was created. Balmon Forum information can be used as a means of communication between members or Balmon Staff.

Liking What You See: A Study on the Motives Behind Instagram Likes

The world has never been more connected than it is today and one key contributor to that fact is the growth of social networking sites. With the sum of about 4.2 billion active users, more than half of the human population, social networking sites have become a highway for information. From holding private conversations to appreciating others’ posts, social media users leave crumbs of information – one of which is a “Like”. ‘Like’ carries a plethora of information, and decoding it offers invaluable insight. This research aims to study the motives behind the act of ‘Liking’ an Instagram post. Following the data collection, an analysis using Albert Bandura’s Social Cognitive Theory will be carried out. With it, the researcher concluded that the motives behind ‘Liking’ an Instagram posts are to manage relationship, form an identity, state affinity, provide support, and prompt similar behavior.Keywords: Social media, Instagram, Instagram Like, Social Cognitive Theory (SCT), Motives

Customer Engagement in Social Media Brand Community

Given the growth of Social Networking Sites (SNS), especially Facebook, companies have commenced engaging with customers in this newly emerged digital space. This study investigates the role of brand trust and brand love in enhancing engagement throughout Facebook. The effect of customer engagement on brand loyalty is also examined. Data was collected using a survey of 400 Syrian fast-food customers. The model is examined by structural equations modeling (SEM). The findings revealed that brand trust and brand love are considered drivers of customer engagement, also brand loyalty is an outcome of customer engagement. Moreover, the finding provides a model which brand managers can follow to build brand loyalty. It will add value in the field of Brand Management.

Fuzzy-Based Trust Model to Evaluate Customer Trust towards Online SNSs Sellers

The rapid growth of Social Networking Sites (SNSs) as business platforms for individual or small sellers recognised trust as the main important role in determining the successful execution of their business operation.Current trust modelfocused in business sellers by considering website as one of the trust factors. However, these model are not applicable for SNSs environment. Based on the identified factors affecting trust in SNSs environment, this paper proposeda fuzzy-based trust model to evaluate customers‘ trust based on their perception and experiences.The evaluation model was then tested to validate its efficiency in evaluating trust level.

Automated image splicing detection using deep CNN-learned features and ANN-based classifier

With the present-day rapid evolution of digital technology, images have become one of the most important means of communication and information carrier in our society. Since the last decade, with the emergence of social networking sites like Facebook, Instagram, Twitter, etc., there has been a huge increase in the amount of information exchanged in the form of digital images, on a regular basis. While traditionally we might have had faith in the credibility of these images, today’s digital technology has begun to erode that faith. Before sharing an image over social networks, editing it with relevant software application has become one of the simplest things to do today. While not many people do this with any sinister intent behind, there has been a significant increase in cybercrimes related to malicious image manipulation and sharing. To this end, image splicing has emerged as one of the major forms of image manipulation attacks, among others today. This demands investigation of intrinsic differences between authentic and forged images and hence development of automated splicing detection tools. Here, we propose a blind image splicing detection technique that employs a deep convolutional residual network architecture as a backbone, followed by a fully connected classifier network, that classifies between authentic and spliced images. The classifier networks have been evaluated using the CASIA v2.0 dataset. Both are proven to yield accuracies more than 96% on an average, having surpassed the state-of-the-art results.

“I Want to See People’s Reactions to the Selfies”: A Lefebvrian Analysis of the Impact of Social Networking Sites on Physical, Mental, and Emotional Functioning

Since the conception and exponential growth of social networking sites (SNSs), technology has advanced sufficiently to allow access to them at any moment for any reason. This has given users a “virtual space” (VS) in which to communicate and “live” within (e.g., Facebook), a space which disparate research has shown to have an impact on users’ behaviors, thoughts, and emotions. The present study aimed to examine the potential for SNSs to influence the physical, mental, and social well-being of undergraduate students. To explore this in a unified fashion, we conducted in-depth interviews with 25 participants across three qualitative studies. All interview transcripts were analyzed using a recursive deductive thematic analysis. Lefebvre’s trialectic of space was examined for its applicability to students’ experiences of VS vis-à-vis SNSs. Lefebvre’s spatial triad provides a novel and coherent framework to untangle and explain the multifaceted and often complicated nature of SNS use. Analysis found correspondence between Lefebvrian triadic space and SNSs to explain the pervasive, dominant, and sometimes pathological role that SNSs can have upon everyday functioning. Implications are that a Lefebvrian approach can inform future research as a means to untangle and explain the multifaceted and often complicated nature of SNS use.

The Prevalence of Addiction to Social Networking Sites and Its Impact on Well-Being among Students of Addis Ababa University, Ethiopia

The emergence of Social Networking Sites (SNS) has been changing many aspects of the students’ daily activities positively. But, inappropriate use of social networking sites may have negative effects like Social Networking Sites Addiction (SNSA) and disturbance of psychological and social well-being. The purpose of this study was to assess the prevalence of SNSA and its impact on well-being among Addis Ababa University, Main Campus students. A cross-sectional research design was employed to collect and analyse data for the study. Simple random sampling technique was used to select participants from the colleges. Two hundred nineteen (290) samples (141 males and 132 females) were selected from the total 1,143 3rd year students in the Campus. A standardized and pilot tested questionnaire was used to collect the data. Descriptive statistics such as percentage and frequency, independent samples t-test, ANOVA, Pearson’s Product-Moment correlation coefficient and linear regression were employed to analyse the data. The finding revealed that majority of the respondents was at the age of 21 - 25. All of the participants included in the analysis were a member of at least one SNS and most of them access SNS several times per day. According to the findings, 3.3% of participants were severely addicted to SNS and there is no gender difference in SNSA. The findings also showed that there was a statistically significant negative correlation between SNSA and both Psychological and Social Well-being. Moreover, recommendations were drawn based on the findings of the study. Keywords: Addiction , Prevalence, Social Networking Sites, Well-being DOI: 10.7176/NMMC/95-01 Publication date: January 31 st 2021

The Prevalence of Addiction to Social Networking Sites and Its Impact on Well-Being among Students of Addis Ababa University, Ethiopia

The emergence of Social Networking Sites (SNS) has been changing many aspects of the students’ daily activities positively. But, inappropriate use of social networking sites may have negative effects like Social Networking Sites Addiction (SNSA) and disturbance of psychological and social well-being. The purpose of this study was to assess the prevalence of SNSA and its impact on well-being among Addis Ababa University, Main Campus students. A cross-sectional research design was employed to collect and analyse data for the study. Simple random sampling technique was used to select participants from the colleges. Two hundred nineteen (290) samples (141 males and 132 females) were selected from the total 1,143 3rd year students in the Campus. A standardized and pilot tested questionnaire was used to collect the data. Descriptive statistics such as percentage and frequency, independent samples t-test, ANOVA, Pearson’s Product-Moment correlation coefficient and linear regression were employed to analyse the data. The finding revealed that majority of the respondents was at the age of 21 - 25. All of the participants included in the analysis were a member of at least one SNS and most of them access SNS several times per day. According to the findings, 3.3% of participants were severely addicted to SNS and there is no gender difference in SNSA. The findings also showed that there was a statistically significant negative correlation between SNSA and both Psychological and Social Well-being. Moreover, recommendations were drawn based on the findings of the study. Keywords: Addiction , Prevalence, Social Networking Sites, Well-being DOI: 10.7176/NMMC/95-01 Publication date: January 31 st 2021