Exploring the hidden patterns of cyberbullying on social media

Singh, Sandeep; Thapar, Vivek; Bagga, Sachin

doi:10.1016/j.procs.2020.03.374

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…A survey of the online activities of 4113 American adults found that social isolation deepened with increasing activities online [22]. A study on teenagers found that Internet use may render them unable to distinguish between the Internet and reality, and make them more susceptible to cyberbullying [23]. The adverse effects of using the Internet on the elderly are more clearly manifested in their physical health.…”

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confidence: 99%

Internet use and need for digital health technology among the elderly: a cross-sectional survey in China

Sun

Yan

Zhou³

et al. 2020

BMC Public Health

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Background China is becoming an aging society at the fastest pace in history, and there are a large number of empty nesters in the country. With economic and social development, internal support systems among families are gradually weakening. Supporting the elderly is thus emerging as a significant issue, and promoting digital health technologies is an effective way to help address it. Encouraging the application of Internet to elderly care and Internet use among the elderly are important means of promoting digital health technologies. This paper examines the current state of the use of the Internet by the elderly and factors influencing it (including physical, psychological, and social) as well as demand among the elderly for smart services. Methods A total of 669 subjects over the age of 60 years were randomly selected from 13 cities in Heilongjiang province and surveyed using questionnaires from May 1 to July 31, 2018. The questionnaires were collected for descriptive statistics, the chi-square test, and the analysis of influential factors. Results Of the people surveyed, 38.6% used the Internet. Their favorite online activity was online dating (74.2%), and the health information they obtained through the Internet was mainly related to diet (63.1%) and exercise (47.1%). The subjects demanded smart bracelets (MD = 2.80) and emergency callers (MD = 2.77). Gender, age, education, monthly income, quality of life, number of friends, and social participation were found to have an impact on Internet use. Conclusions More measures are needed to reduce barriers to the use of the Internet and promote digital health technologies. The society, equipment manufacturers, and family members of the elderly should work together to enable them to reap the benefits of online technologies.

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confidence: 99%

Internet use and need for digital health technology among the elderly: a cross-sectional survey in China

Sun

Yan

Zhou³

et al. 2020

BMC Public Health

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“…two words that appear together the most) makes it easier to establish the most important combinations of the words for sentiment analysis. The word pairs that appear most of the time, in the tweets, are placed in the same clusters and the word pairs that have fewer combinations are placed in other clusters (Singh et al, 2020). Thus, in this study, word pairs were visualised to gain a better understanding of SNA for imported Twitter data since there were more than 40 000 word-pair edges in each of our networks.…”

Section: Visualisation Of Word Pairsmentioning

confidence: 99%

COVID‐19 pandemic and food poverty conversations: Social network analysis of Twitter data

2022

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This novel and mixed‐method study investigated food poverty conversations at the beginning of the COVID‐19 pandemic and the subsequent national lockdown on the social media platform Twitter. NodeXL Pro software was used to collect tweets using the terms ‘food’ and ‘poverty’ in any order somewhere in a tweet sent on selected days between April 5 and May 23, 2020. The data obtained from NodeXL Pro were cleaned. Social network analysis tools were used to analyse and visualise our data. Using this method, sentiment‐related words (positive or negative words), the top (the most mentioned) 10 hashtags, top words and top word pairs were identified. The patterns of word pairs communicated in our network were visualised based on each word pair's frequency. This also enabled us to carry out a content analysis to create coding of the word pairs’ data. A total of 81 249 tweets were identified that contained the terms ‘food’ and ‘poverty’. Our findings revealed that individuals’ tweets overwhelmingly contained views about the increase in hunger, food poverty and food insecurity due to the COVID‐19 pandemic. Twitter users perceived that when the pandemic measures began, many food‐secure families were pushed into food insecurity due to a rapid rise in unemployment and rising poverty due to the quarantine and stay‐at‐home instructions in place at the time. They also addressed the sharp rise in food poverty being driven by panic buying, food shortages, food affordability and disruptions in food supply and food systems. Our analysis of this data suggests that to mitigate food poverty or to prevent a ‘hunger pandemic’ for future pandemic emergencies, comprehensive and longer term policy responses and economic supports are needed to strengthen the resilience of food systems. However, the highlighted limitations of this study must be considered.

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“…The first relies on social network analysis (SNA) ( Wang & Lee, 2021 ). The study of Singh, Thapar & Bagga (2020) exemplifies SNA usage and content-based analysis to detect cyberbullying (a form of toxic behavior) on Twitter. The study investigates the Momo Challenge, a fake challenge spread on Facebook and other social media platforms to entice younger users to commit violent acts.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating toxicity changes of cross-community redditors from 2 billion posts and comments

Almerekhi

Kwak

Jansen

2022

PeerJ Computer Science

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This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the same community, across multiple communities, and over time. As for the toxicity detection performance, the BERT model achieved a 91.27% classification accuracy and an area under the receiver operating characteristic curve (AUC) score of 0.963 and outperformed several baseline machine learning and neural network models. The user behavior toxicity analysis showed that 16.11% of users publish toxic posts, and 13.28% of users publish toxic comments. However, results showed that 30.68% of users publishing posts and 81.67% of users publishing comments exhibit changes in their toxicity across different communities, indicating that users adapt their behavior to the communities’ norms. Furthermore, time series analysis with the Granger causality test of the volume of links and toxicity in user content showed that toxic comments are Granger caused by links in comments.

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Exploring the hidden patterns of cyberbullying on social media

Cited by 13 publications

References 14 publications

Internet use and need for digital health technology among the elderly: a cross-sectional survey in China

Internet use and need for digital health technology among the elderly: a cross-sectional survey in China

COVID‐19 pandemic and food poverty conversations: Social network analysis of Twitter data

Investigating toxicity changes of cross-community redditors from 2 billion posts and comments

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