Books by Michal Ptaszynski
Due to the prevalence of social network service and social media, the problem of cyberbullying ha... more Due to the prevalence of social network service and social media, the problem of cyberbullying has risen to the forefront as a major social issue over the last decade. Internet hate, harassment, cyberstalking, cyberbullying—these terms, which were almost unknown 10 years ago—are in the everyday lexicon of all internet users today. Unfortunately, it is becoming increasingly difficult to undertake continuous surveillance of websites as new ones are appearing daily. Methods for automatic detection and mitigation for online bullying have become necessary in order to retain comfortable user experience online.
Automatic Cyberbullying Detection: Emerging Research and Opportunities provides innovative insights into online bullying and methods of early identification, mitigation, and prevention of harassing speech and activity online. The book provides explanations and reasoning for each of these applied methods and discusses their pros and cons int he context of the language of online bullying. Also included are some generalizations of cyberbullying as a phenomenon and how to approach the problem from a practical technology-backed point of view. The content within this publication represents the work spanning over ten years and covers a wide range of artificial intelligence, machine learning and natural language processing methods applied to the problem of automatic cyberbullying detection, such as traditional machine learning, deep learning, web mining, or language combinatorics. The book is designed for researchers, academicians, social media moderators, IT consultants, programmers and education administrators and covers topics centered on methods of detection and mitigation of cyberbullying, and surrounding problems such as internet hate and online harassment.
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This book describes my research on enhancing machines with Emotional Intelligence. I develop a se... more This book describes my research on enhancing machines with Emotional Intelligence. I develop a set of affect analysis tools and propose methods for their efficient utilization. The first system, ML-Ask, separates emotive utterances from neutral and in the emotive utterances seeks for expressions of specific emotion types. The second system, CAO, extracts emoticons from input and determines the emotion types they express. The above systems are then utilized in two methods for enhancing of Human-Computer Interaction. The first is a method for automatic evaluation of conversational agents. In this method the information on user emotional engagement during conversation is reinterpreted to specify general attitudes to conversational agents. The second method determines whether emotions expressed by speaker are appropriate for the context of the conversation. The information on affective states of the user-speaker is confronted with gathered from the Internet list of emotions that should be expressed at the moment. I conclude the book with a discussion on other applications for the proposed methods and further work needed for full implementation of Emotional Intelligence in machines.
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From the beginning of computer era over half a century ago, humanity was fascinated by the idea o... more From the beginning of computer era over half a century ago, humanity was fascinated by the idea of creating a machine substituting their mental capabilities. This New Age version of Mary Shelley's Frankenstein gave birth to S-F literature and was one of the motors for development of our civilisation. The mental functions digitalized as the first ones were fast processing of large numbers or sophisticated formulas for specialized fields like mathematics or physics. These functions were the most troublesome for humans, but the easiest to process mechanically. Ironically, the human mental functions said to be the most human-like, and thought of as the ones which make up a grown well-socialized man, such as a sense of humour or understanding emotions of others, were neglected in Computer Science for a long time as too subjective and therefore unscientific...
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Papers by Michal Ptaszynski
International Journal of Environmental Research and Public Health
In this paper, we study language used by suicidal users on Reddit social media platform. To do th... more In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported t...
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Ainu is a critically endangered language spoken by the native inhabitants of northern Japan. This... more Ainu is a critically endangered language spoken by the native inhabitants of northern Japan. This paper describes our research aimed at the development of technology for automatic processing of text in Ainu. In particular, we improved the existing tools for normalizing old transcriptions, word segmentation, and part-of-speech tagging. In the experiments we applied two Ainu language dictionaries from different domains (literary and colloquial) and created a new data set by combining them. The experiments revealed that expanding the lexicon had a positive impact on the overall performance of our tools, especially with test data unrelated to any of the training sets used.
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Word segmentation is an essential task in automatic language processing for languages where there... more Word segmentation is an essential task in automatic language processing for languages where there are no explicit word boundary markers, or where space-delimited orthographic words are too coarse-grained. In this paper we introduce the MiNgMatch Segmenter—a fast word segmentation algorithm, which reduces the problem of identifying word boundaries to finding the shortest sequence of lexical n-grams matching the input text. In order to validate our method in a low-resource scenario involving extremely sparse data, we tested it with a small corpus of text in the critically endangered language of the Ainu people living in northern parts of Japan. Furthermore, we performed a series of experiments comparing our algorithm with systems utilizing state-of-the-art lexical n-gram-based language modelling techniques (namely, Stupid Backoff model and a model with modified Kneser-Ney smoothing), as well as a neural model performing word segmentation as character sequence labelling. The experiment...
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Cognitive Systems Research
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Journal of Open Research Software
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Proceedings of the 2008 Ieee Wic Acm International Conference on Web Intelligence and Intelligent Agent Technology Volume 03, 2008
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Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems Volume 1 Volume 1, 2010
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Transactions of the Japanese Society For Artificial Intelligence, 2010
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... In our previous works we showed that implementing a very simple pun genera-tor into a chatter... more ... In our previous works we showed that implementing a very simple pun genera-tor into a chatterbot can visibly improve its performance. ... One of the first and probably most robust sys-tems in the field of pun processing is Binsted's JAPE punning riddles generator (Binsted, 1996 ...
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人工知能学会全国大会論文集, 2008
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Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, Jul 12, 2012
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Books by Michal Ptaszynski
Automatic Cyberbullying Detection: Emerging Research and Opportunities provides innovative insights into online bullying and methods of early identification, mitigation, and prevention of harassing speech and activity online. The book provides explanations and reasoning for each of these applied methods and discusses their pros and cons int he context of the language of online bullying. Also included are some generalizations of cyberbullying as a phenomenon and how to approach the problem from a practical technology-backed point of view. The content within this publication represents the work spanning over ten years and covers a wide range of artificial intelligence, machine learning and natural language processing methods applied to the problem of automatic cyberbullying detection, such as traditional machine learning, deep learning, web mining, or language combinatorics. The book is designed for researchers, academicians, social media moderators, IT consultants, programmers and education administrators and covers topics centered on methods of detection and mitigation of cyberbullying, and surrounding problems such as internet hate and online harassment.
Papers by Michal Ptaszynski
Automatic Cyberbullying Detection: Emerging Research and Opportunities provides innovative insights into online bullying and methods of early identification, mitigation, and prevention of harassing speech and activity online. The book provides explanations and reasoning for each of these applied methods and discusses their pros and cons int he context of the language of online bullying. Also included are some generalizations of cyberbullying as a phenomenon and how to approach the problem from a practical technology-backed point of view. The content within this publication represents the work spanning over ten years and covers a wide range of artificial intelligence, machine learning and natural language processing methods applied to the problem of automatic cyberbullying detection, such as traditional machine learning, deep learning, web mining, or language combinatorics. The book is designed for researchers, academicians, social media moderators, IT consultants, programmers and education administrators and covers topics centered on methods of detection and mitigation of cyberbullying, and surrounding problems such as internet hate and online harassment.