Interdisciplinary Contributions to Flame Modeling

Pazienza, Maria Teresa; Tudorache, Alexandra Gabriela

doi:10.1007/978-3-642-23954-0_21

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Pendar [6] uses lexical features with machine learning classifiers to differentiate victims from predators in online chatting environment. Pazienza and Tudorache [9] propose utilizing user profiling features to detect aggressive discussions. They use users' online behavior histories (e.g., presence and conversations) to predict whether or not users' future posts will be offensive.…”

Section: B) User-level Offensiveness Detectionmentioning

confidence: 99%

Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

Chen

Zhou

Zhu

et al. 2012

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

482

341

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Abstract-Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content, and user-level offensiveness evaluation is still an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media. We distinguish the contribution of pejoratives/profanities and obscenities in determining offensive content, and introduce hand-authoring syntactic rules in identifying name-calling harassments. In particular, we incorporate a user's writing style, structure and specific cyberbullying content as features to predict the user's potentiality to send out offensive content. Results from experiments showed that our LSF framework performed significantly better than existing methods in offensive content detection. It achieves precision of 98.24% and recall of 94.34% in sentence offensive detection, as well as precision of 77.9% and recall of 77.8% in user offensive detection. Meanwhile, the processing speed of LSF is approximately 10msec per sentence, suggesting the potential for effective deployment in social media.

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Section: B) User-level Offensiveness Detectionmentioning

confidence: 99%

Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

Chen

Zhou

Zhu

et al. 2012

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

482

341

View full text Add to dashboard Cite

show abstract

“…Mac OS X is a series of Unix‐based graphical user interface OS developed by Apple r. Mac OS X was the successor of Mac OS 9, which was released in 1999. The concise statements of the evolving Apple OS is listed as follows: Mac OS X 10.0 (Cheetah, released in 2001); Mac OS X 10.1 (Puma, released in 2001); Mac OS X 10.2 (Jaguar, released in 2002); Mac OS X 10.3 (Panther, released in 2003); Mac OS X 10.4 (Tiger, released in 2005); Mac OS X 10.5 (Leopard, released in 2007); Mac OS X 10.6 (Snow Leopard, released in 2009); Mac OS X 10.7 (Lion, released in 2011); Mac OS X 10.8 (Mountain Lion, released in 2012); Mac OS X 10.9 (Mavericks, released in 2013); Mac OS X 10.10 (Yosemite, released in 2014); Mac OS X 10.11 (EI Capitan, released in 2015), respectively .…”

Section: Literature Reviewsmentioning

confidence: 99%

Testifying the digital artifacts for line application program under Mac OS X from the aspects of witness experts

Chu

Chao

2016

Security Comm Networks

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Unarguably, the physical memory acquisition of contemporary computing devices has been an extremely pivotal and indispensable errand for digital forensics specialists because of its volatility of the embedded data, which was deposited from related computing resources. Nowadays, hundreds of millions of global naive or sophisticated end users exploit LINE application program (AP) as the instant messaging toolkit to conduct real-time communications including sending text messages or video clips. Unfortunately, this cutting-edge innovation has been taken advantage by the cybercrime syndicates, terrorists, or the extremists to fulfill their lucrative purposes. Hence, this research provides generic research methodologies for digital forensics experts to conduct whenever digital artifacts become mandatory in order to disclose some cybercrimes or the noncompliance of computer usage concerning LINE AP especially for the witness experts to testify in a court of law. On the other hand, this research paper pinpoints the volatile raw memory contains valuable and imperceptive digital artifacts that could reflect the interactions when LINE AP was utilized under Mac OS X. Copyright

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“…The majority of the existing technical studies on cyberbullying have concentrated on the detection of bullying or harassing comments [4][5][6], while there is hardly work on the more challenging task of detecting cyberbullies and studies for this area of research are largely missing. There are few exceptions however, that point out an interesting direction for the incorporation of user information in detecting offensive contents, but more advanced user information or personal characteristics such as writing style or possible network activities has not been included in these studies [7,8]. Cyberbullying prevention based on user profiles was addressed for the first time in our latest study in which an expert system was developed that assigns scores to social network users to indicate their level of 'bulliness' and their potential for future misbehaviour based on the history of their activities [9].…”

Section: Introductionmentioning

confidence: 99%

Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

Dadvar

Trieschnigg

Jong

2014

Advances in Artificial Intelligence

110

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Abstract. Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of "bulliness" of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance. IntroductionWith the growth of the use of Internet as a social medium, a new form of bullying has emerged, called cyberbullying. Cyberbullying is defined as an aggressive, intentional act carried out by a group or individual, using electronic forms of contact repeatedly and over time against a victim who cannot easily defend him or herself [1]. One of the most common forms is the posting of hateful comments about someone in social networks. Many social studies have been conducted to provide support and training for adults and teenagers [2,3]. The majority of the existing technical studies on cyberbullying have concentrated on the detection of bullying or harassing comments [4-6], while there is hardly work on the more challenging task of detecting cyberbullies and studies for this area of research are largely missing. There are few exceptions however, that point out an interesting direction for the incorporation of user information in detecting offensive contents, but more advanced user information or personal characteristics such as writing style or possible network activities has not been included in these studies [7,8]. Cyberbullying prevention based on user profiles was addressed for the first time in our latest study in which an expert system was developed that assigns scores to social network users to indicate their level of 'bulliness' and their potential for future misbehaviour based on the history of their activities [9]. In the previous work we did not investigate machine learning models. In this study we focus again on the detection of bully users in online social networks but now we look into the efficiency of both expert systems and machine learning models for identifying the potential bully users. We compare the performance of both systems for the task of assigning a score to social network users that indicates their level of bulliness. We demonstrate that the expert system outperforms the machine learner and can be effectively combined in a hybrid classifier. The approach we propose can be used for building monitoring tools to stop potential bullies from conducting further harm. Data Collection and Feature SelectionIn this section we will explain the characteristics of the corpus used in this study. We also describe the feature space and the three feature categories that have been used...

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Interdisciplinary Contributions to Flame Modeling

Cited by 6 publications

References 18 publications

Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

Testifying the digital artifacts for line application program under Mac OS X from the aspects of witness experts

Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

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