scholarly journals Proactively Discouraging Cyberbullying Activities

2021 ◽  
Vol 9 (10) ◽  
pp. 1601-1607
Author(s):  
Puneetha KR
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Leisure Activities ◽  
Cyber Bullying ◽  
Future Research ◽  
Internet Users ◽  
The Social ◽  
Social Media Platforms ◽  
Root Word ◽  
Cyberbullying Detection

Abstract: Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. Cyber bullying is one of the most common problems faced by the internet users making internet a vulnerable space hence there has to be some detection that is needed on the social media platforms. Detecting the bullies online at the earliest makes sure that these platforms are safer for the user and internet indeed becomes a platform to share information and use it for other leisure activities. Even though there has been some research going on implementing detection and prevention of cyber bullying, it is not completely feasible due to certain limitations imposed. In this paper lexicon-based approach of the NLTK sentiwordnetis used to differentiate the positive and negative words and produce results. These words are given negative and positive values greater than or less than zero for positive and negative words respectively. Lexicon based systems utilize word lists and use the presence of words within the lists to detect cyberbullying. Lemmatization is used to find the root word. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in thisfield. Keywords: Abuse and crime involving computers, natural language processing, sentiment analysis, social networking

“Digital buddies”: parasocial interactions in social media

2016 ◽  
Vol 10 (4) ◽  
pp. 305-320 ◽  
Author(s):  
Mujde Yuksel ◽  
Lauren I. Labrecque
Keyword(s):  
Social Media ◽  
Student Athletes ◽  
Future Research ◽  
Content Type ◽  
Social Media Platform ◽  
The Social ◽  
Social Media Platforms ◽  
Media Platform ◽  
Personal Accounts ◽  
Parasocial Interactions

Purpose This paper aims to focus its inquiries on the parasocial interactions (PSI) and relationships (PSR) consumers form with personae in online social media communities. The authors extend the marketing literature on parasocial interaction/relationship beyond brands by focusing on personal social media accounts (public student-athletes). Design/methodology/approach The authors adopt a grounded theory methodology (Glaser and Strauss, 2009) triangulating observational netnographic data (Kozinets, 2010) of 49 public student-athlete accounts on Twitter (34,500 tweets) with in-depth interviews. The findings emphasize that PSI/PSR occur not only from interactions with brands but also through personal accounts on social media platforms. Findings The investigation reveals that through such social media platforms, PSI/PSR influence consumers cognitively, affectively and behaviorally. In terms of cognition, the data suggest that PSI/PSR can influence opinion, interests, attention allocation and construction of relations, specifically through the availability of in-depth knowledge about the social media persona. Additionally, the research findings indicate that affect-laden messages from persona can alter emotion and mood, induce empathetic reactions and trigger inspiration, especially in relation to the shared interest of the online community of the social media account. Behaviorally, the findings suggest that personas’ messages can direct and inspire both online and offline actions through endorsed behavioral parasocial interactions. Research limitations/implications This research focused on one specific social media platform, Twitter. Twitter was specifically chosen, because it is a popular social media platform and allows non-reciprocal relationships. Although the authors feel that the findings would hold for other social media platforms, future research may be conducted to see if there are differences in PSI/PSR development on different types of networks. Additionally, the authors focused on a specific type of personal account, student-athletes. Future research may wish to extend beyond this population to other personal social media accounts, such as fashion bloggers, diy bloggers and others. Originality/value This research reveals that PSI/PSR can occur not only from interactions with brands but also through personal accounts on social media platforms. The findings give support for the value of brand spokespersons and brand ambassadors and suggest that brands should take careful consideration into who is chosen to represent the brand.

scholarly journals Applying Social Media in Crisis Communication: A Quantitative Review of Social Media-Related Crisis Communication Research from 2009 to 2017

2017 ◽  
Vol 1 (1) ◽  
pp. 29-37
Author(s):  
Yuan Wang ◽  
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Crisis Communication ◽  
Theoretical Models ◽  
Research Trend ◽  
Future Research ◽  
Communication Research ◽  
The Social ◽  
Social Media Platforms ◽  
Future Research Directions

Social media has drawn growing attention from crisis communication researchers. The purpose of this study was to provide an overview of the current paradigm of research on social media and crisis communication, to identify the research gaps, and to help scholars understand future research directions in this area. The current study examined the trends and patterns of social media-related crisis communication research published in 11 communication and public relations journals from 2009 to 2017. More specifically, it focused on the trends and characteristics of research topics, theories and theoretical models, crisis types, social media platforms, sample types, and research methods. This study found that public relations-focused journals published most of the social media-related crisis communication articles. Most studies adopted theories or theoretical models and examined the role of social media in crisis communication, which focused on product tampering and general crisis. Additionally, a considerable number of studies employed content analysis techniques that used social media content as the sample. This study discussed the trends of social media-related crisis communication research and the directions for future research. Keywords: Crisis communication, social media, research trend, public relations, communication.

Illusions of Empowerment

2012 ◽  
Vol 2 (1) ◽  
pp. 36-51 ◽  
Author(s):  
Corinne Weisgerber
Keyword(s):  
Social Media ◽  
New Media ◽  
The Public ◽  
Media Technologies ◽  
Digital Democracy ◽  
Internet Users ◽  
Attention Economy ◽  
The Social ◽  
Social Media Platforms ◽  
Media Systems

This article calls into question the social media empowerment narrative and the underlying idea that social media platforms are empowering everyday netizens to have their voices heard. The author argues that social media technologies may simply privilege only those Internet users who are new media savvy and have leisure time to participate in the so-called digital democracy. While social media systems might have lowered the entrance threshold for civic engagement, hurdles such as the growing competition in an attention economy, the odds of standing out amidst millions of other individual voices, knowledge of new media technologies required to achieve visibility, and time demands make the social media empowerment vision more difficult to attain than the architects of the empowerment ideology have made the public to believe.

Measuring the impact of COVID-19 papers on the social web: an altmetric study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Metwaly Ali Mohamed Edakar ◽  
Ahmed Maher Khafaga Shehata
Keyword(s):  
Social Media ◽  
Social Impact ◽  
Service Providers ◽  
Research Performance ◽  
Future Research ◽  
Content Type ◽  
The Social ◽  
Social Media Platforms ◽  
Number Of Citations ◽  
The Impact

Purpose The rapid spread and severity of the coronavirus (COVID-19) virus have prompted a spate of scholarly research that deals with the pandemic. The purpose of this study is to measure and assess the coverage of COVID-19 research on social media and the engagement of readers with COVID-19 research on social media outlets. Design/methodology/approach An altmetric analysis was carried out in three phases. The first focused on retrieving all papers related to COVID-19. Phase two of the research aimed to measure the presence of the retrieved papers on social media using altmetric application programming interface (API). The third phase aimed to measure Mendeley readership categories using Mendeley API to extract data of readership from Mendeley for each paper. Findings The study suggests that while social media platforms do not give accurate measures of the impact as given by citations, they can be used to portray the social impact of the scholarly outputs and indicate the effectiveness of COVID-19 research. The results confirm a positive correlation between the number of citations to articles in databases such as Scopus and the number of views on social media sites such as Mendeley and Twitter. The results of the current study indicated that social media could serve as an indicator of the number of citations of scientific articles. Research limitations/implications This study’s limitation is that the studied articles’ altmetrics performance was examined using only one of the altmetrics data service providers (altmetrics database). Hence, future research should explore altmetrics on the topic using more than one platform. Another limitation of the current research is that it did not explore the academic social media role in spreading fake information as the scope was limited to scholarly outputs on social media. The practical contribution of the current research is that it informs scholars about the impact of social media platforms on the spread and visibility of COVID-19 research. Also, it can help researchers better understand the importance of published COVID-19 research using social media. Originality/value This paper provides insight into the impact of COVID-19 research on social media. The paper helps to provide an understanding of how people engage with health research using altmetrics scores, which can be used as indicators of research performance.

Visual Representations of Indigenous Tourism Places in Social Media

2021 ◽  
Vol 21 (2) ◽  
pp. 95-108
Author(s):  
Susanna Heldt Cassel ◽  
Cecilia De Bernardi
Keyword(s):  
Social Media ◽  
Visual Communication ◽  
Media Analysis ◽  
Media Representations ◽  
Future Research ◽  
Social Media Analysis ◽  
The Social ◽  
Social Media Platforms ◽  
Indigenous Tourism

This article focused the analysis on social media representations of Sápmi using the hashtags #visitsápmi and #visitsapmi, which nuance official, top-down versions of the place communicated in other contexts, but simultaneously are more focused on visitors and their experiences. The results show that the making of the Sápmi region as a place and a tourism destination through social media content is an ongoing process of interpretation and reinterpretation of what indigenous Sámi culture is and how it connects to specific localities. Future research should look at the broader understanding of places that can be accessed through social media analysis. The main argument is that visual communication is a very important tool when constructing the brand of a destination. Considering the growing role of social media, the process of place-making through visual communication is explored in the case of the destination VisitSápmi, as it is coconstructed in online user generated content (UGC). From a theoretical viewpoint, we discuss the social construction of places and destinations as well as the production of meaning through coconstruction of images and brands in tourism contexts. The focus is on how places are created, branded, and made meaningful by visualizing the place in a framework of tourism experiences, in this case specifically examined through indigenous tourism. We use a content analysis of texts, photographs, and narratives communicated on social media platforms. Regardless of negotiated brand management's efforts at official marketing, branding, and tourism planning, the evolution of Sápmi as a place to visit in social media has its own logic, full of contradictions and plausible interpretations, related to the uncontrollable and bottom-up processes of UGC.

A STUDY ON DEEP LEARNING ALGORITHMS FOR MULTIMODAL AND MULTILINGUAL CYBERBULLYING DETECTION

2021 ◽  
pp. 50-54
Author(s):  
Vijayakumar V ◽  
Hari Prasad D
Keyword(s):  
Deep Learning ◽  
Text Messages ◽  
Cyber Bullying ◽  
Physical And Mental Health ◽  
Modal Data ◽  
Learning Techniques ◽  
The Social ◽  
Social Media Platforms ◽  
Cyberbullying Detection ◽  
Multiple Languages

With the increased utilization of the internet and social media platforms, can foster destructive or harmful behaviors such as cyberbullying. Cyberbullying poses signicant threat to physical and mental health of the victims. There is a demand for automatic detection and prevention of cyberbullying. In Social networks, there is a big challenge to detect the cyber bullying event and to control all the cyberbullying content and languages that users post. Due to complexity of multiple languages and cross-mix languages used in cyberbullying, the detection has remained only mildly satisfying. And also recently, images and videos dominate the social feeds in addition to text messages and comments. Machine learning and deep learning techniques can be helpful to detect the bullies and can generate a model to automatically detect multi-lingual cyberbullying actions. Deep neural architectures are useful to model, learn and fuse multi-modal data for cyber bullying detection. This paper proposes a detailed review on machine and deep learning approach for detecting and preventing multimodal and multilingual cyberbullying.

An exploration of logistics-related customer service provision on Twitter

2016 ◽  
Vol 46 (6/7) ◽  
pp. 659-680 ◽  
Author(s):  
Jyotirmoyee Bhattacharjya ◽  
Adrian Ellison ◽  
Sonali Tripathi
Keyword(s):  
Social Media ◽  
Customer Service ◽  
Service Providers ◽  
Third Party ◽  
Future Research ◽  
Content Type ◽  
The Social ◽  
Specific Order ◽  
Independent Observations ◽  
Social Media Platforms

Purpose – The success of e-retailers is intrinsically linked to the effectiveness of their logistics processes which, inevitably, involve third party service providers. As the most tangible representative of the e-retailers it is inevitable that customers expect the e-retailer to resolve delivery queries, including on social media platforms. The purpose of this paper is to investigate the effectiveness of e-retailers’ logistics-related customer service interactions on Twitter with a view towards identifying effective and ineffective social media customer service strategies. Design/methodology/approach – The design and public nature of Twitter encourages organic conversations between e-retailers and customers as well as between customers and other customers. The methodology applied here accounts for this by collecting and analysing interactions within and as part of conversations, not as independent observations. In total, 203,349 tweets were collected from 22 of the most popular e-retailers. A random sample of 5,000 logistics-related conversations (16,998 tweets) is used for the analysis presented here and forms a foundation for future research. Findings – Conversations are initiated by customers on the basis of 24 event triggers which can be categorised as occurring either before or after an order is delivered. These can be general queries or related to a specific order or delivery issue. The paper identifies a number of significant findings such as the extent to which e-retailers and logistics providers redirect customers to other channels to resolve queries, ignoring the implicit preference by customers to use Twitter to resolve their problem. Similarly, the lack of interactions between e-retailers and their logistics providers within the Twitter platform to help resolve customer queries results in ineffective customer service. Practical implications – The study identifies the way in which e-retailers can substantially improve the effectiveness of the customer service they provide on Twitter by ensuring that customer queries can be resolved within the platform and by working with their logistics partners to do the same. This is critical since problems may be directed to the e-retailer or the logistics provider but both companies jointly suffer the consequences of poor customer service. Originality/value – The study examines a hitherto underexplored aspect of retail logistics – the social media-based customer service activities of e-retailers. Methodologically, the study is rooted in the acknowledgement that interactions on Twitter form conversations and analyses should take this into account. This is a distinctly different approach from existing Twitter-related studies which conduct an automated sentiment analysis of tweets. This approach reveals a rich picture of interactions and, importantly, identifies where conversations between e-retailers begin, how they develop and how they conclude.

scholarly journals Malicious SPAM Injection Attack Detection on Social Webpage Posts

2020 ◽  
Author(s):  
Arul E ◽  
Punidha A
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Engineering ◽  
Attack Detection ◽  
Test Results ◽  
Malicious Attacks ◽  
Corporate Websites ◽  
Internet Users ◽  
The Social ◽  
Social Media Platforms

The social media platforms for teens and genz are highly influential; 39% state that they will use’ buy buttons’ and 25% use smartphones for shopping images. In the meantime, 28 percent of US internet users between 18 and 55 years of age said their aim is to buy via social media during holidays. As these channels become more central to our everyday lives, social media platforms have now become a key vector of attack that businesses cannot neglect anymore. Social media Platforms provide up to 20% more options for delivering malware for consumers, such as advertising, social engineering, equities and plug-ins compare to eCommerce and corporate websites. The suggested version Supervised SD-LVQ used to detect malicious firmware on various social media sites. LVQ classifies the different service calls attacks associated with XML, HTML, JavaScript files and different forms of malicious attacks on social networks. The test results show that 98.70% is genuinely positive and 0.02% is falsely negative.

scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

2021 ◽  
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2664
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

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