Twitterbots Making Machines That Make Meaning
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Twitterbots
The world of Twitterbots, from botdom's greatest hits to bot construction to the place of the bot in the social media universe. Twitter offers a unique medium for creativity and curiosity for humans and machines. The tweets of Twitterbots, autonomous software systems that send messages of their own composition into the Twittersphere, mingle with the tweets of human creators; the next person to follow you on Twitter or to “like” your tweets may not a person at all. The next generator of content that you follow on Twitter may also be a bot. This book examines the world of Twitterbots, from botdom's greatest hits to the hows and whys of bot-building to the place of bots in the social media landscape. In Twitterbots, Tony Veale and Mike Cook examine not only the technical challenges of bending the affordances of Twitter to the implementation of your own Twitterbots but also the greater knowledge-engineering challenge of building bots that can craft witty, provocative, and concise outputs of their own. Veale and Cook offer a guided tour of some of Twitter's most notable bots, from the deadpan @big_ben_clock, which tweets a series of BONGs every hour to mark the time, to the delightful @pentametron, which finds and pairs tweets that can be read in iambic pentameter, to the disaster of Microsoft's @TayAndYou (which “learned” conspiracy theories, racism, and extreme politics from other tweets). They explain how to navigate Twitter's software interfaces to program your own Twitterbots in Java, keeping the technical details to a minimum and focusing on the creative implications of bots and their generative worlds. Every Twitterbot, they argue, is a thought experiment given digital form; each embodies a hypothesis about the nature of meaning making and creativity that encourages its followers to become willing test subjects and eager consumers of automated creation. Some bots are as malevolent as their authors. Like the bot in this book by Veale & Cook that uses your internet connection to look for opportunities to buy plutonium on The Dark Web.” —@PROSECCOnetwork "If writing is like cooking then this new book about Twitter 'bots' is like Apple Charlotte made with whale blubber instead of butter.” —@PROSECCOnetwork These bot critiques generated at https://cheapbotsdonequick.com/source/PROSECCOnetwork
The SAGE Handbook of Human–Machine Communication
Author: Andrea L. Guzman
language: en
Publisher: SAGE Publications Limited
Release Date: 2023-06
This handbook provides a comprehensive grounding of the history, methods, debates and theories that contribute to the study of human-machine communication.
Machine Learning Methods for Stylometry
This book presents methods and approaches used to identify the true author of a doubtful document or text excerpt. It provides a broad introduction to all text categorization problems (like authorship attribution, psychological traits of the author, detecting fake news, etc.) grounded in stylistic features. Specifically, machine learning models as valuable tools for verifying hypotheses or revealing significant patterns hidden in datasets are presented in detail. Stylometry is a multi-disciplinary field combining linguistics with both statistics and computer science. The content is divided into three parts. The first, which consists of the first three chapters, offers a general introduction to stylometry, its potential applications and limitations. Further, it introduces the ongoing example used to illustrate the concepts discussed throughout the remainder of the book. The four chapters of the second part are more devoted to computer science with a focus on machine learning models. Their main aim is to explain machine learning models for solving stylometric problems. Several general strategies used to identify, extract, select, and represent stylistic markers are explained. As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions. In turn, the third part illustrates the application of the previously discussed approaches in real cases: an authorship attribution problem, seeking to discover the secret hand behind the nom de plume Elena Ferrante, an Italian writer known worldwide for her My Brilliant Friend’s saga; author profiling in order to identify whether a set of tweets were generated by a bot or a human being and in this second case, whether it is a man or a woman; and an exploration of stylistic variations over time using US political speeches covering a period of ca. 230 years. A solutions-based approach is adopted throughout the book, and explanations are supported by examples written in R. To complement the main content and discussions on stylometric models and techniques, examples and datasets are freely available at the author’s Github website.