The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Christine Neylon Obrien - One of the best experts on this subject based on the ideXlab platform.
-
twenty first century labor law striking the right balance between workplace civility rules that accommodate equal employment opportunity obligations and the loss of protection for concerted activities under the national labor relations act
2020Co-Authors: Christine Neylon ObrienAbstract:This article outlines the current state of the law regarding conduct that, while otherwise protected by Section 7 of the National Labor Relations Act, nonetheless involves workplace Profanity or offensive speech that potentially violates employer civility rules and equal employment opportunity laws, whether at work, on social media, or on a picket line. The paper considers recent appellate court and National Labor Relations Board (NLRB) decisions on this important issue, highlighting the NLRB’s own reconsideration of its standards as announced in its call for amicus briefs in the General Motors case, September 2019. The author recommends a solution that balances the important public policies underlying both the National Labor Relations Act and equal employment opportunity laws, as well as employer and employee rights to manage and work in a place with a desired level of respect and consideration for others.
-
i swear from shoptalk to social media the top ten national labor relations board Profanity cases
St. John’s Law Review, 2016Co-Authors: Christine Neylon ObrienAbstract:Two waitresses at Hooters got into a swearing match with the waitress who won a mandatory bikini competition that was rumored to have been rigged in favor of the winner. The two losers were terminated for yelling obscenities at their winning coworker in front of customers. An off duty barista at a New York Starbucks repeatedly used Profanity in a heated conversation with a manager in the presence of customers, and was fired for his conduct.
Ashley M Fraser - One of the best experts on this subject based on the ideXlab platform.
-
Profanity in media associated with attitudes and behavior regarding Profanity use and aggression
Pediatrics, 2011Co-Authors: Sarah M Coyne, Laura Stockdale, David A Nelson, Ashley M FraserAbstract:We hypothesized that exposure to Profanity in media would be directly related to beliefs and behavior regarding Profanity and indirectly to aggressive behavior. METHODS: We examined these associations among 223 adolescents attending a large Midwestern middle school. Participants completed a number of questionnaires examining their exposure to media, atti- tudes and behavior regarding Profanity, and aggressive behavior. RESULTS: Results revealed a positive association between exposure to Profanity in multiple forms of media and beliefs about Profanity, pro- fanity use, and engagement in physical and relational aggression. Spe- cifically, attitudes toward Profanity use mediated the relationship be- tween exposure to Profanity in media and subsequent behavior involving Profanity use and aggression. CONCLUSIONS: The main hypothesis was confirmed, and implications for the rating industry and research field are discussed. Pediatrics 2011;128:000
L David - One of the best experts on this subject based on the ideXlab platform.
-
anti Profanity laws and the first amendment
Social Science Research Network, 2017Co-Authors: L DavidAbstract:The essay first examines several current state laws that prohibit Profanity under certain circumstances. It then details a few recent cases in which individuals were convicted for uttering Profanity. The next section explains how Profanity can be a part of an unprotected category of speech, such as fighting words, true threats, or harassment. Finally, the essay examines whether such laws and cases comport with First Amendment principles.
Shivakant Mishra - One of the best experts on this subject based on the ideXlab platform.
-
Prediction of cyberbullying incidents in a media-based social network
2016 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016Co-Authors: Homa Hosseinmardi, Rahat Ibn Rafiq, Qin Lv, Shivakant MishraAbstract:Cyberbullying is a major problem affecting more than half of all American teens. Prior work has largely focused on detecting cyberbullying after the fact. In this paper, we investigate the prediction of cyberbullying incidents in Instagram, a popular media-based social network. The novelty of this work is building a predictor that can anticipate the occurrence of cyberbullying incidents before they happen. The Instagram media-based social network is well-suited to such prediction since there is an initial posting of an image typically with an associated text caption, followed later by the text comments that form the basis of a specific cyberbullying incident. We extract several important features from the initial posting data for automated cyberbullying prediction, including Profanity and linguistic content of the text caption, image content, as well as social graph parameters and temporal content behavior. Evaluations using a real-world Instagram dataset demonstrate that our method achieves high performance in predicting the occurrence of cyberbullying incidents.
-
analyzing labeled cyberbullying incidents on the instagram social network
Social Informatics, 2015Co-Authors: Homa Hosseinmardi, Rahat Ibn Rafiq, Qin Lv, Sabrina Arredondo Mattson, Shivakant MishraAbstract:Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to study labeled cyberbullying incidents in the Instagram social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying. A detailed analysis of the labeled data is then presented, including a study of relationships between cyberbullying and a host of features such as cyberaggression, Profanity, social graph features, temporal commenting behavior, linguistic content, and image content.
-
prediction of cyberbullying incidents on the instagram social network
arXiv: Information Retrieval, 2015Co-Authors: Homa Hosseinmardi, Rahat Ibn Rafiq, Qin Lv, Sabrina Arredondo Mattson, Shivakant MishraAbstract:Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect and predict incidents of cyberbullying in Instagram, a media-based mobile social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying. A detailed analysis of the labeled data is then presented, including a study of relationships between cyberbullying and a host of features such as cyberaggression, Profanity, social graph features, temporal commenting behavior, linguistic content, and image content. Using the labeled data, we further design and evaluate the performance of classifiers to automatically detect and pre- dict incidents of cyberbullying and cyberaggression.
Mia Consalvo - One of the best experts on this subject based on the ideXlab platform.
-
good clean fun a content analysis of Profanity in video games and its prevalence across game systems and ratings
Cyberpsychology Behavior and Social Networking, 2009Co-Authors: James D Ivory, Dmitri Williams, Nicole Martins, Mia ConsalvoAbstract:Although violent video game content and its effects have been examined extensively by empirical research, verbal aggression in the form of Profanity has received less attention. Building on preliminary findings from previous studies, an extensive content analysis of Profanity in video games was conducted using a sample of the 150 top-selling video games across all popular game platforms (including home consoles, portable consoles, and personal computers). The frequency of Profanity, both in general and across three Profanity categories, was measured and compared to games' ratings, sales, and platforms. Generally, Profanity was found in about one in five games and appeared primarily in games rated for teenagers or above. Games containing Profanity, however, tended to contain it frequently. Profanity was not found to be related to games' sales or platforms.
