During the last years, there has been a growing multidisciplinary interest in alternative educational approaches, such as serious games, aiming at enhancing thinking skills and media literacy. Likewise, the objective of this study is to present the design and the development of an educational web application for learning the necessary steps towards the detection of bogus content, according to the fact-checking procedures. The game presents news articles, which have to be characterized as fake or real by the players. During the effort to reach the correct decision, the players can use tools and practices for identifying relevant information regarding the clues, which frame a news story (title, date, creator, source, containing images). After presenting the progress of interface design and development, this paper reports the results of a randomized online field study (n = 111), which provides some preliminary evidence. Specifically, it is validated that the game can raise awareness, teach about authentication tools, and highlight the importance of patterns that include evidence regarding the authenticity of articles. Additionally, thorough discussion was conducted within a media class (n = 35) to receive useful feedback/evaluation about the offered utilities and their usability. The findings suggest that educational games may be a promising vehicle to inoculate the public against misinformation.

Emotional speech is a separate channel of communication that carries the paralinguistic aspects of spoken language. Affective information knowledge can be crucial for contextual speech recognition, which can also provide elements from the personality and psychological state of the speaker enriching the communication. That kind of data may play an important role as semantic analysis features of web content and would also apply in intelligent affective new media and social interaction domains. A...

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.

Semantic audio analysis has become a fundamental task in modern audio applications, making the improvement and optimization of classification algorithms a necessity. Standard frame-based audio classification methods have been optimized, and modern approaches introduce engineering methodologies that capture the temporal dependency between successive feature observations, following the process of temporal feature integration. Moreover, the deployment of the convolutional neural networks defined...

This paper investigates methods aiming at the automatic recognition and classification of discrete environmental sounds, for the purpose of subsequently applying these methods to the recognition of soundscapes. Research in audio recognition has traditionally focused on the domains of speech and music. Comparatively little research has been done towards recognizing non-speech environmental sounds. For this reason, in this paper, we apply existing techniques that have been proved efficient in the other two domains. These techniques are comprehensively compared to determine the most appropriate one for addressing the problem of environmental sound recognition.

Hate speech spreading online is a matter of growing concern since social media allows for its rapid, uncontrolled, and massive dissemination. For this reason, several researchers are already working on the development of prototypes that allow for the detection of cyberhate automatically and on a large scale. However, most of them are developed to detect hate only in English, and very few focus specifically on racism and xenophobia, the category of discrimination in which the most hate crimes are recorded each year. In addition, ad hoc datasets manually generated by several trained coders are rarely used in the development of these prototypes since almost all researchers use already available datasets. The objective of this research is to overcome the limitations of those previous works by developing and evaluating classification models capable of detecting racist and/or xenophobic hate speech being spread online, first in Spanish, and later in Greek and Italian. In the development of these prototypes, three differentiated machine learning strategies are tested. First, various traditional shallow learning algorithms are used. Second, deep learning is used, specifically, an ad hoc developed RNN model. Finally, a BERT-based model is developed in which transformers and neural networks are used. The results confirm that deep learning strategies perform better in detecting anti-immigration hate speech online. It is for this reason that the deep architectures were the ones finally improved and tested for hate speech detection in Greek and Italian and in multisource. The results of this study represent an advance in the scientific literature in this field of research, since up to now, no online anti-immigration hate detectors had been tested in these languages and using this type of deep architecture.