Unravelling the basic concepts and intents of misbehavior in post-truth society

Objective: To explore the definitions and connections between the terms misinformation, disinformation, fake news, rumors, hoaxes, propaganda and related forms of misbehavior in the online environment. Anotherobjective is to infer the intent of the authors, where relevant.Design/Methodology/Approach: A conceptual analysis of three hundred fifty articles or monographies from all types of disciplines with a priority of the articles focused on terminological analysis was being utilized. A conceptual map of the terminology that is relevant to the post-truth era was created. In the case of the lack of agreement, the etymology of the terms, utilizing dictionaries, terminological databases and encyclopedias,was favored.Results/Discussion: The approach made possible to delimit the borders between the core terms of posttruth society and to classify them according to the intents of the authors: power (influence), money, fun, sexual harassment, hate/discord, ignorance, passion and socialization. These features were identified to be able to differentiate the concepts: falsity (misleadingness, deceptiveness, lack of verification), accuracy, completeness, currency, medium, intent and analyzable unit. The conceptual map, summarizing and visualizing our findings is attached in the article.Conclusions: We argued that disinformation and misinformation are different terms with different authors and intents in the online environment. Likewise, fake news was delimitated as species of disinformation, which is limited by the medium and financial intent. The intent of hoaxers is rather the amusement of the authors or to spread discord between different groups of society. The intent and analyzable units as statement, claim, article, message, event, story and narrative that were identified in the literature, are crucial for the understanding and communication between social (human) scientists and computer scientists in order to better detect and mitigate various types of false information.Originality/Value: The study provides a theoretical background for detecting, analyzing and mitigating false information and misbehavior

Contract for the Lease of Enterprise

Tato práce popisuje Smlouvu o nájmu podniku podle § 488b až § 488i obchodního zákoníku.Katedra soukromého práva a civilního procesuObhájenoContract for the Lease of Enterprise is concieved in the Czech Commercial Code. Enterprise is the collective think, on which aplly special legal regime. It consist of tangible, intangible and personal parts. This diploma explains proces, how this contract can become legal with all its deifferencies from the other contracts included in the Commercial Code

MultiClaim: Multilingual Previously Fact-Checked Claim Retrieval

MultiClaim: Multilingual Previously Fact-Checked Claim Retrieval - is a dataset that can be used to train a test models used for disinformation combatting. The dataset consists of 206k claims fact-checked by professional fact-checkers and 28k social media posts gathered from the wild. Each social media post has at least on claim assigned. The main idea is to develop information retrieval models that will assign appropriate claims to all the posts. GitHub repository: https://github.com/kinit-sk/multiclaim Contents fact_check_post_mapping.csv - Mapping between fact checks and social media posts: fact_check_id post_id fact_checks.csv - Data about fact-checks: fact_check_id claim - This is the translated text (see below) of the fact-check claim instances - Instances of the fact-check – a list of timestamps and URLs. title - This is the translated text (see below) of the fact-check title posts.csv - Data about social media posts: post_id instances - Instances of the fact-check – a list of timestamps and what were the social media platforms. ocr - This is a list of translated texts (see below) of the OCR transcripts based on the images attached to the post. verdicts - This is a list of verdicts attached by Meta (e.g., False information) text - This is the translated text (see below) of the text written by the user. What is a translated text? A tuple of text, its translation to English and detected languages, e.g., in the sample below we have an original Croatian text, its translation to English and finally the predicted language composition (hbs = Serbo-Croatian): ( '"...bolnice su pune ? ti ina, muk...upravo sada, bolnica Rebro..tragi no sme no', '"...hospitals are full? silence, silence... right now, Rebro hospital... tragically funny', [('hbs', 1.0)] ) More details TB