Analysis of the Effectiveness of Promotion Strategies of Social Platforms for the Elderly with Different Levels of Digital Literacy

Xu, Xiaoyan; Mei, Yi; Sun, Yanhong; Zhu, Xin‐Guang

doi:10.3390/app11094312

Cited by 4 publications

(2 citation statements)

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“…Nearly half of the active older people use social networks almost every day. In addition, [29] [30] claimed that technology is essential for the elderly to gain online media literacy, keep themselves safe while using the internet and creatively access data in the virtual world. They were preparing to live life to the fullest in a way that benefits them, their family, and society.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Elderly Use of Digital Technology in Thailand

Chanyawudhiwan

Mingsiritham

2022

Int. J. Interact. Mob. Technol.

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The elderly population is on the rise, globally. It is an age group that should live to their fullest potential to enhance the quality of their life, should be prepared to live life to the fullest in a way that benefits them, their family, and society. The elderly should be able to access digital technology and benefit from it. Therefore, it is necessary to educate the elderly choosing the right kind of digital technology to their quality of life, and protect themselves from cyber threats. This study performs an exploratory factor analysis of digital technology competency. A multistage random sampling method was used to obtain a sample of 225 elderly people in Thailand. The tool used was a questionnaire on digital technology competency, which included 45 questions on a 5-point scale. Data were analyzed using descriptive statistics and exploratory factor analysis. The exploratory factor analysis of digital technology competency for the elderly in Thailand found six important components: 1) use of mobile operating systems, 2) data file management, 3) cloud data management, 4) use of support applications, 5) use of applications for communication, and 6) use of technology for security. Thus, the elderly need to develop online media literacy to safely navigate the Internet while creatively using data found in the virtual domain

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Section: Discussionmentioning

confidence: 99%

Analysis of Elderly Use of Digital Technology in Thailand

Chanyawudhiwan

Mingsiritham

2022

Int. J. Interact. Mob. Technol.

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“…Numbers of social media users in India have grown substantially in recent years, despite the low quality of internet services and the occasional interruptions or blocking of social media sites in the country. Multifarious populations in the country have been using online social media to communicate, express opinions, engage with friends, and share information [2][3][4]. However, the anonymity and mobility of online social media enable the netizens behind the screen to easily spread hateful content [5,6].…”

Section: Introductionmentioning

confidence: 99%

Automatic Hate Speech Detection in English-Odia Code Mixed Social Media Data Using Machine Learning Techniques

Mohapatra

Prasad

Bebarta³

et al. 2021

Applied Sciences

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Hate speech on social media may spread quickly through online users and subsequently, may even escalate into local vile violence and heinous crimes. This paper proposes a hate speech detection model by means of machine learning and text mining feature extraction techniques. In this study, the authors collected the hate speech of English-Odia code mixed data from a Facebook public page and manually organized them into three classes. In order to build binary and ternary datasets, the data are further converted into binary classes. The modeling of hate speech employs the combination of a machine learning algorithm and features extraction. Support vector machine (SVM), naïve Bayes (NB) and random forest (RF) models were trained using the whole dataset, with the extracted feature based on word unigram, bigram, trigram, combined n-grams, term frequency-inverse document frequency (TF-IDF), combined n-grams weighted by TF-IDF and word2vec for both the datasets. Using the two datasets, we developed two kinds of models with each feature—binary models and ternary models. The models based on SVM with word2vec achieved better performance than the NB and RF models for both the binary and ternary categories. The result reveals that the ternary models achieved less confusion between hate and non-hate speech than the binary models.

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