scholarly journals How Malicious Comments on Social Media Have a Huge Impact on the Negative Attitude Towards Women

2021 ◽  
Vol 11 (9) ◽  
Author(s):  
HUANG Puiyu
Keyword(s):  
Social Media ◽  
Negative Attitude ◽  
Huge Impact

Electronic Trading, Electronic Advertising, and Social Media Literacy

2020 ◽  
pp. 224-261
Author(s):  
Yurdagül Meral ◽  
Duygu Ecem Özbay
Keyword(s):  
Social Media ◽  
Media Literacy ◽  
Digital Marketing ◽  
The Internet ◽  
Local Market ◽  
Electronic Trade ◽  
Large Audience ◽  
Huge Impact ◽  
Electronic Advertising ◽  
Marketing Methods

The internet has a huge impact on everything including by converting traditional trade methods into electronic trade and traditional marketing/advertising methods into electronic advertising and digital marketing methods not only in local trade but international trade as well. The purpose of this study is to increase literacy about electronic advertising, social media, digital marketing, by giving examples of how Turkish local influencers are used in social media to increase sales of international products. As a result, it is seen that the advertising posts made by influencers could reach a large audience in the local market.

scholarly journals The Dynamic Evolution Mechanism of Heterogeneous OWOM—An Improved Viral Marketing Model

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 140
Author(s):  
Mengjie Liao ◽  
Lin Qi ◽  
Jian Zhang
Keyword(s):  
Social Media ◽  
Negative Impact ◽  
Viral Marketing ◽  
Dynamic Visualization ◽  
Agent Based ◽  
Marketing Model ◽  
Online Word Of Mouth ◽  
The Social ◽  
Huge Impact ◽  
Multi Agent

The negative impact of brand negative online word-of-mouth (OWOM) on social images in social media is far greater than the promotion of positive OWOM. Thus, how to optimize brand image by improving the positive OWOM effect and slowing the negative OWOM communication has turned into an urgent problem for brand enterprises. On this basis, we analyze the evolution process of the OWOM communication group of the social media brand network based on the SOR (stimulus-organism-response) theory of psychology. Through constructing the heterogeneous brand OWOM communication dynamic model and conducting the multi-agent-based simulation experiment, the dynamic visualization of brand OWOM communication effect combined the thinking model of viral marketing is realized. Experiments show that the ability of brand communicators to persuade has a direct impact on the persistence and breadth of brand communication. When the acceptance of the consumer market is high, the negative OWOM of the brand has a relatively huge impact on consumers.

scholarly journals Identification of Online Public Shaming Using Machine Learning Framework

2021 ◽  
pp. 539-543
Author(s):  
Sonali Gaikwad ◽  
Tejashri Borate ◽  
Nandpriya Ashtekar ◽  
Umadevi Lade
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Negative Impact ◽  
Daily Life ◽  
Irrelevant Information ◽  
Social Media Platform ◽  
Learning Framework ◽  
Huge Impact ◽  
Social Media Platforms ◽  
Media Platform

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platform in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonouring comments, tweets towards people are categorized into 9 types. The tweets are further classifies into one of these types or non-shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share are probably going to modify the person in question. Moreover, it is not the nonshaming devotee who checks the increment quicker but of shaming in twitter.

scholarly journals “The Social Media Blue Bird Creating an Online Sensation”: A Sentimental Analysis with Reference to Coke-studio Hashtags

2019 ◽  
Vol 18 (1) ◽  
pp. 15-33
Author(s):  
Anand Shankar Raja Ram ◽  
Amaravathi M
Keyword(s):  
Social Media ◽  
Brand Image ◽  
Consumer Preferences ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Differential Approach ◽  
The Social ◽  
The World ◽  
Huge Impact ◽  
Media Platform

Coke-Studio, an online platform hosted in YouTube has been successful in both countries and has been discussed on another popular social media platform Twitter. However, studies report that Pakistan Coke-studio fares better in terms of its emotive content than its Indian edition. The paper analyses how the ―Hashtag fever‖ which has created a huge impact on brand image and profitability position of firms all around the world leads to the differential approach. Though a detailed Social Media Analysis (SMA), this paper aims to examine how hashtags work on the Twitter platforms and conclude how social-media often offers a glimpse of subconscious consumer preferences and its implications thereof.

scholarly journals One Step Forward, Two Tweets Back: Exploring Cultural Backlash and Hockey Masculinity on Twitter

2020 ◽  
pp. 1-11
Author(s):  
Daniel Sailofsky ◽  
Madeleine Orr
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Sociological Theory ◽  
Negative Attitude ◽  
The Political ◽  
Rule Changes ◽  
One Step ◽  
Professional Hockey ◽  
Political Realm ◽  
Insight Into

Between 2000 and 2018, the number of fights in professional hockey decreased by more than half, reflecting rule changes intended to preserve player health. A 2019 playoff fight ignited debate on social media over the place of fighting in hockey. This research involved a content analysis of an incendiary tweet and the 920 replies it solicited. Content analysis confirmed that cultural backlash exists in sport and provided insight into manifestations of backlash. Comments exhibiting backlash varied by subject (i.e., what or who is being discussed in the tweet) and attitude (i.e., positive approval for fighting and negative attitude toward change), with many defending hockey masculinity. Connections are drawn to manifestations of backlash in the political realm, the extant hockey masculinity literature, and implications for sociological theory and the sport of hockey are discussed.

Analysis of Facts for Companies Going Public through Social Media

2013 ◽  
pp. 36-45
Author(s):  
Dilip Kumar Mallick ◽  
Susmi Routray
Keyword(s):  
Social Media ◽  
Market Analysis ◽  
Secondary Market ◽  
Social Connections ◽  
Going Public ◽  
Huge Impact ◽  
Commercial Environment

Social media is altering interpersonal and social connections, empowering new forms of commitment and contribution. The emergence of social media has a dramatic impact on the current commercial environment. The power of authority has shifted from business to potential consumers. Going wrong in social media can have a huge impact on a business. However, at the same time, the huge potential of the medium cannot be overlooked. In this chapter, the authors compile a review of the cases where companies have undervalued or misused the power of social media. The research is basically a secondary market analysis wherein the failures have been identified and analyzed. Based on the analysis of the cases, the chapter suggests elucidation for companies going public through social media.

Comparative Analyses of Terrorist Threats under Conditions of COVID-19

2020 ◽  
Vol 64 (11) ◽  
pp. 5-12
Author(s):  
G. Machitidze
Keyword(s):  
Social Media ◽  
Young People ◽  
Target Selection ◽  
Modus Operandi ◽  
Daily Lives ◽  
Terrorist Organizations ◽  
Global Pandemic ◽  
Terrorist Groups ◽  
Informal Economies ◽  
Huge Impact

The coronavirus pandemic has already caused high numbers of deaths, massive economic disruption, and had a huge impact on daily lives of millions of people. In short, – this is everything terrorist organizations affiliated with the ISIL and Al-Qaida would want to achieve with their attacks. The global pandemic serves as inspiration and influences the modus operandi of terrorists, both in terms of tactics and target selection. Since the pandemic started, the ISIL and Al-Qaida have persisted in operations across African, Asian and European countries, leaving hundreds of people dead. These attacks clearly show that terrorism has continued worldwide during the COVID 19 pandemic. Terrorist groups are looking at ways to take advantage of instability, increase recruitment and sympathizers among the vulnerable and terrified, encourage conventional and even biological attacks. Terrorist networks have encouraged followers to weaponise their own illness by trying to infect others. Terrorist groups also use the pandemic to gain visibility. The ISIL has been exploiting hashtags related to the coronavirus to redirect users to its radical propaganda. Terrorists have also found inspiration in the global pandemic with regard to their target selection, in particular critical health infrastructures. In Afghanistan, the Taliban has adopted a different approach to COVID 19 assisting efforts to limit the spread of the virus, including by declaring ceasefires in areas under its control. Hezbollah is working to fight the virus and maintain order within Lebanon. Because they control informal economies, groups like the Taliban or Lebanon’s Hezbollah are better placed to benefit from coronavirus than the ISIL or Al-Qaida. In general, terrorist groups are taking advantage of COVID 19 lockdowns to spread hatred and intensify social media efforts to recruit young people spending more time online.

scholarly journals Perceptions of Using Social Media as an ELT Tool among EFL Teachers in the Saudi Context

2016 ◽  
Vol 9 (7) ◽  
pp. 1 ◽  
Author(s):  
Madawi Allam ◽  
Tariq Elyas
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Foreign Language ◽  
Teaching And Learning ◽  
Quantitative Methods ◽  
Collective Intelligence ◽  
Tertiary Institutions ◽  
Media Technologies ◽  
Efl Teachers ◽  
Huge Impact

<p>Social media technologies have undeniably become an integral part of people’s lives and they have been widely used amonsgest the new genrations, particularly, university students. This widespread of social media technologies has certainly made a huge impact on the way people learn and interact with each other resulting in the emergence of communities of learning that are supported by collective intelligence. This study is based on quantitative methods using a survey instrument to gather descriptive data regarding the perceptions of seventy-five (<em>n=75) </em>randomly chosen male and female English as a Foreign Language (EFL) teachers at two Saudi tertiary institutions. The study utilized a 14 Likert scale statements where each statement had five Likert-type items for the participants to choose from. Analysis of the gathered data indicated that the majority of the participants believe strongly in the pedagoocal values and benefits of using social media as an ELT tool in the EFL classes in the Saudi context. Nevertheless, the majority expressed reservations with regards to the extent to which social media can be freely allowed to be used in the EFL classroom where they perceive it as having a double edged sword effect and that is mainly due to some undesired distractions that some students may resort to which may occasionaly result in the opposite of the intended effect of their usage. The study recommends more research studies in this area so as to closely understand how experienced EFL teachers utilize social media in their classes in order to develop best practices for implementing social media in teaching and learning in EFL in the Saudi contexts</p>

scholarly journals Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Mudasir Ahmad Wani ◽  
Nancy Agarwal ◽  
Patrick Bours
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Detection System ◽  
Online Social Network ◽  
Negative Attitude ◽  
Health Crisis ◽  
Online Groups ◽  
Detection Model ◽  
Twitter Users ◽  
The Impact

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain.

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