Fast learning for sentiment analysis on bullying

Xu, Junming; Zhu, Xiaojin; Bellmore, Amy

doi:10.1145/2346676.2346686

Cited by 45 publications

(44 citation statements)

References 31 publications

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“…On an individual basis: anger, negative feelings and feelings of inferiority, feeling demotivated, feelings of fear and intimidation, feeling emotional and upset, irritation, loss of self-esteem, stress and wasted time. These findings are in line with previous research (Xu & Zhu, 2012;Lam & Li, 2013);…”

Section: Effects Of Cyber Incivilitysupporting

confidence: 94%

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Exploring the Impact of Cyber Incivility in the Workplace

2015

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The world is an interconnected global village due to the increasing adoption and reliance on technology, but an ugly side of the increased usage of technology has come to light. The issue of harassment and abuse on the internet has led to relatively new issues such as cyber harassment, cyber incivility and cyberbullying.A case study was conducted within two faculties at the University of Cape Town (UCT). The research objectives were: to find out how staff members in a workplace have experienced cyber incivility, to find out what effects cyber incivility has on employees, to find out what the motivations are for staff participation in cyber incivility, and to find out what policies a workplace should have in place in order to deal with cyber incivility.The data collected shows that there have been occurrences of cyber harassment and cyber incivility among staff members at UCT. The following effects were found to be consistent with cyber harassment and cyber incivility: decrease in productivity and a toxic working environment. On an individual basis: anger, negative feelings and feelings of inferiority, feeling demotivated, feelings of fear and intimidation, feeling emotional and upset, irritation, loss of self-esteem, stress and wasted time.

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Section: Effects Of Cyber Incivilitysupporting

confidence: 94%

“…Emotions that are associated with cyberbullying victims are anger, embarrassment, empathy, fear, pride, relief (when the situation is resolved) and sadness (Xu & Zhu, 2012). Victims also display bad moods (Tokunaga, 2010).…”

Section: Effects and Impactmentioning

confidence: 99%

Exploring the Impact of Cyber Incivility in the Workplace

2015

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“…Dadvar et al [10] detected cyberbullies instead of detecting text content; Nahar et al [21] used social networks to present a graph model, identifying the most active cyberbullying predators and victims; Xu et al [31] explored the detection of roles within cyberbullying, and identified those of bully, victim, accuser and reporter. In addition, research [30,13,29] used Latent Dirichlet Allocation (LDA) to extract the main topics for each text content.…”

Section: Related Workmentioning

confidence: 99%

Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media

Chen¹,

McKeever²,

Delany³

2016

Advances in Intelligent Systems and Computing

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The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat -using datasets from Twitter, YouTube, MySpace, Kongregate, Formspring and Slashdot. Using supervised machine learning, we compare alternative text representations and dimension reduction approaches, including feature selection and feature enhancement, demonstrating the impact of these techniques on detection accuracies. In addition, we investigate the need for sampling on imbalanced datasets. Our conclusions are: (1) Dataset balancing boosts accuracies significantly for social media abusive content detection; (2) Feature reduction, important for large feature sets that are typical of social media datasets, improves efficiency whilst maintaining detection accuracies; (3) The use of generic structural features common across all our datasets proved to be of limited use in the automatic detection of abusive content. Our findings can support practitioners in selecting appropriate text mining strategies in this area.

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“…Their tasks consisted of obtaining traces via the twitter streaming API to find an instance of the word "bully" in tweets and build an eight classes text classifier based on pre-defined emotion classes [15]. Henri et al (2012) showed that it is possible to predict real-world threats by extracting abnormalities in tweets [16].…”

Section: Xu Et Al (2012) Have Detected Traces Of Bullying On Thementioning

confidence: 99%

A Novel Position-based Sentiment Classification Algorithm for Facebook Comments

Surroop¹,

Canoo²,

Pudaruth³

2016

ijacsa

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Abstract-With the popularization of social networks, people are now more at ease to share their thoughts, ideas, opinions and views about all kinds of topics on public platforms. Millions of users are connected each day on social networks and they often contribute to online crimes by their comments or posts through cyber bullying, identity theft, online blackmailing, etc. Mauritius has also registered a surge in the number of cybercrime cases during the past decade. In this study, a trilingual dataset of 1031 comments was extracted from public pages on Facebook. This dataset was manually categorized into four different sentiment classes: positive, negative, very negative and neutral, using a novel sentiment classification algorithm. Out of these 1031 comments, it was found that 97.8% of the very negative sentiments, 70.7% of the negative sentiments and 77.0% of the positive sentiments were correctly extracted. Despite the added complexity of our dataset, the accuracy of our system is slightly better than similar works in the field. The accuracy of the lexicon-based approach was also much higher than when we used machine learning techniques. The outcome of this research work can be used by the Mauritius Police Force to track down potential cases of cybercrime on social networks. Decisive actions can then be implemented in time.

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Fast learning for sentiment analysis on bullying

Cited by 45 publications

References 31 publications

Exploring the Impact of Cyber Incivility in the Workplace

Exploring the Impact of Cyber Incivility in the Workplace

Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media

A Novel Position-based Sentiment Classification Algorithm for Facebook Comments

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