Recognizing both literal and figurative meanings is crucial to understanding users’ opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic comments in the form of tweets often include positive words that represent negative or undesirable characteristics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to understand the application of different machine learning algorithms for sarcasm detection in Twitter. Extensive database searching led to the inclusion of 31 studies classified into two groups: Adapted Machine Learning Algorithms (AMLA) and Customized Machine Learning Algorithms (CMLA). The review results revealed that Support Vector Machine (SVM) was the best and the most commonly used AMLA for sarcasm detection in Twitter. In addition, combining Convolutional Neural Network (CNN) and SVM was found to offer a high prediction accuracy. Moreover, our result showed that using lexical, pragmatic, frequency, and part-of-speech tagging can contribute to the performance of SVM, whereas both lexical and personal features can enhance the performance of CNN-SVM. This work also addressed the main challenges faced by prior scholars when predicting sarcastic tweets. Such knowledge can be useful for future researchers or machine learning developers to consider the major issues of classifying sarcastic posts in social media.
Purpose The purpose of this paper is to address the major findings of published research on the factors influencing students’ knowledge building in an online collaborative environment. Design/methodology/approach The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used to review and synthesize existing empirical studies on knowledge building in a collaborative learning context. In total, 24 studies were identified from major electronic bibliographic databases. The research was conducted between 2017 and 2019. Results of these studies were analyzed to determine potential factors that may influence the knowledge-building process among students. Findings Factors related to interaction and participation, task, student and support were found to be the major factors driving students’ knowledge building in the online collaborative learning environment. The association between these factors and certain collaborative tasks was mapped. Originality/value Findings from this review can help decision makers of higher education in both developing and developed countries to take the necessary steps in order to promote effective knowledge-building practices in online collaborative learning. It may also help educational policy makers to understand the particulars of collaborative knowledge-building practices, so to increase organizational overall effectiveness and performance.
In 1985 Kenya hosted the UN World Congress on Women to tackle gender-based violence (GBV) and enhance women’s standing generally, but recently, in 2014, the country experienced a wave of public stripping of women – some captured on video and distributed via social media. These human rights violations, especially in Nairobi, happened in a nation that has witnessed three decades of pro-women activism bloom and whose constitution prescribes gender equity, with recent laws toughening punishment for sexual offences. Yet similarly worrying waves of GBV have occurred in other African countries. Focusing on Kenya, South Africa and Nigeria, we use constructivist theory of framing and discourse analyses of media and interview texts to examine the extent to which purposively selected digital era change agents use mainstream and social media to aid critical literacy in their capacitybuilding bid to alter retrogressive attitudes harmful to women’s rights and progress.
For many years, certain climatic factors have been used to predict potential disease outcomes of relevance to humans. This is because early discovery of disease (or its symptoms) would help people or healthcare professionals to take the necessary precautions. Since microblogs can be used to create new connections and maintain existing relationships, disease detection in microblogs is still considered a serious problem for many healthcare systems, especially for establishing a successful epidemic recognition procedure. To tackle this issue, this study proposed a novel tracking approach to diagnose illnesses in microblogs. It is based on the interconnection between certain emotional type and climatic factors associated with a specific disease (e.g., migraine). In this study, detailed migraine data were collected from Twitter. We used K-means and Apriori algorithms to extract migraine-related emotions and investigate the potential associations between migraine symptoms and climatic factors. The results showed that sad emotions were highly interrelated with migraine symptoms. The classification results showed that Sequential Minimal Optimization (SMO) was efficient (95.53% accuracy) in detecting the migraine symptoms from Twitter. The proposed mechanism can be used efficiently in biosurveillance systems due to its capability in identifying the hidden symptoms of a sickness on microblogs. This study paves the way to discover disease-related features using both emotional and climatic factors.
PurposeNews research scholars define immediacy as constant news updating, whereas scholars in other fields conceptualize it more broadly as meaning closeness. The present study explicates the concept of immediacy and proposes a multidimensional notion of news immediacy that reflects physical and psychological closeness to the news.Design/methodology/approachA scale for measuring multifaceted immediacy was developed and tested in a between-subjects design experiment. Four dimensions were extracted from the analysis: transportation, involvement, vividness and timeliness.FindingsThe results reveal greater immediacy in online than print news contexts. Involvement is key to the experience of immediacy in both contexts; yet the feeling of being transported to the places of the news events was stronger among online than print news users. The latter relied more on vividness of the news presentation to attain closeness to the news.Originality/valueImplications of the study were discussed.
Narcissism is increasingly being regarded as one of the most serious sociocultural problemsof the contemporary era. Indeed, recent studies by Baldwin and Stroman (2007) and Buffardiand Campbell (2008), among others, have advanced the opinion that new media technologies– particularly social networking websites – have significantly exacerbated the rise and spread ofnarcissism in contemporary society. Based on this premise that social media provide the perfectplatform for the promotion of self-infatuation, this research paper provides a critical analysis of thepotential influence of social media in the development of a widespread narcissistic socioculturalcondition. In this regard, claims that increasingly consumerist, individualist and media-saturatedsocieties are nurturing a culture of extreme narcissism, vanity and entitlement are examined inrelation to an increase in the use of consumer-orientated new media technologies. In particular,by examining the structural components of the popular social networking site, Facebook,this research highlights the connection between the use of this form of new media and theengenderment of an acutely consumerist and narcissistic subjectivity. That is, the role of newmedia technologies in the promotion of narcissistic identity construction is examined as a factor ofparticular significance in the formation of contemporary subjectivity. In relation to this, the impactof online narcissism on the perpetuation and propagation of capitalist isolation, alienation andinsecurity is investigated before some remedial measures ‒ which co-opt rather than negate suchsocial media ‒ are proposed.
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