Cyberbullying is a risk associated with the online safety of young people and, in this paper, we address one of its most common implicit forms -negation-based forms. We first describe the role of negation in public textual cyberbullying interaction and identify the cyberbullying constructions that characterise these forms. We then formulate the overall detection mechanism which captures the three necessary and sufficient elements of public textual cyberbullying -the personal marker, the dysphemistic element, and the link between them. Finally, we design rules to detect both overt and covert negation-based forms, and measure their effectiveness using a development dataset, as well as a novel test dataset, across several metrics: accuracy, precision, recall, and the F1-measure. The results indicate that the rules we designed closely resemble the performance of human annotators across all measures.

<div data-canvas-width="619.2967614992452">In this paper we investigate the contribution of previous discourse in identifying elements that are key to detecting public textual cyberbullying. Based on the analysis of our dataset, we first discuss the missing cyberbullying elements and the grammatical structures representative of discourse-dependent cyberbullying discourse. Then we identify four types of discourse dependent cyberbullying constructions: (1) fully inferable constructions, (2) personal marker and cyberbullying link inferable constructions, (3) dysphemistic element and cyberbullying link inferable constructions, and (4) dysphemistic element inferable constructions. Finally, we formalise a framework to resolve the missing cyberbullying elements that proposes several resolution algorithms. The resolution algorithms target the following discourse dependent message types: (1) polarity answers, (2) contradictory statements, (3) explicit ellipsis, (4) implicit affirmative answers, and (5) statements that use indefinite pronouns as placeholders for the</div><div data-canvas-width="146.57988069516077">dysphemistic element.</div>

Public textual cyberbullying has become one of the most prevalent issues associated with online safety of young people, particularly on social networks. To address this issue, we argue that the boundaries of what constitutes public textual cyberbullying needs to be first identified and a corresponding linguistically motivated definition needs to be advanced. Thus, we propose a definition of public textual cyberbullying that contains three necessary and sufficient elements: the personal marker, the dysphemistic element and the cyberbullying link between the previous two elements. Subsequently, we argue that one of the cornerstones in the overall process of mitigating the effects of cyberbullying is the design of a cyberbullying lexical database that specifies what linguistic and cyberbullying specific information is relevant to the detection process. In this vein, we propose a novel cyberbullying lexical database based on the definition of public textual cyberbullying. The overall architecture of our cyberbullying lexical database is determined semantically, and, in order to facilitate cyberbullying detection, the lexical entry encapsulates two new semantic dimensions that are derived from our definition: cyberbullying function and cyberbullying referential domain. In addition, the lexical entry encapsulates other semantic and syntactic 1

Identifying the characters from free-form text and understanding the roles and relationships between them is an evolving area of research. They have a wide range of applications, from summarising narrations to understanding the social network from social media tweets, which can help in automation and improve the experience of AI systems like chatbots and much more. The aim of this research is twofold. Firstly, we aim to develop an effective method of extracting characters from a story summary, to develop a set of relevant features, then, using supervised learning algorithms, to identify the character types. Secondly, we aim to examine the efficacy of unsupervised learning algorithms in type identification, as it is challenging to find a dataset with a predetermined list of characters, roles, and relationships that are essential for supervised learning. To do so, we used summary plots of fictional stories to experiment and evaluate our approach. Our character extraction approach successfully improved on the performance reported by existing work, with an average F1-score of 0.86. Supervised learning algorithms successfully identified the character types and achieved an overall average F1-score of 0.94. However, the clustering algorithms identified more than three clusters, indicating that more research is needed to improve their efficacy.

No reproduction, copy or transmission may be made without written permission from the individual authors. Review ProcessPapers submitted to this conference have been double-blind peer reviewed before final acceptance to the conference. Initially, abstracts were reviewed for relevance and accessibility and successful authors were invited to submit full papers. Many thanks to the reviewers who helped ensure the quality of all the submissions. Ethics and Publication Malpractice PolicyACPIL adheres to a strict ethics and publication malpractice policy for all publications -details of which can be found here: http://www.academic-conferences.org/policies/ethics-policy-for-publishing-in-the-conferenceproceedings-of-academic-conferences-and-publishing-international-limited/ Self-Archiving and Paper RepositoriesWe actively encourage authors of papers in ACPIL conference proceedings and journals to upload their published papers to university repositories and research bodies such as ResearchGate and Academic.edu. Full reference to the original publication should be provided. Conference ProceedingsThe Conference Proceedings is a book published with an ISBN and ISSN. The proceedings have been submitted to a number of accreditation, citation and indexing bodies including Thomson ISI Web of Science and Elsevier Scopus.Author affiliation details in these proceedings have been reproduced as supplied by the authors themselves.