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The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions.

The aim of this study is to investigate how amplifiers are used in Twitter (now called “X”) during election campaigns. Specifically, the main purpose is to identify the role and engagement of Twitter amplifiers in the 2019 European elections, the visibility of political parties and leaders, and the way in which automated tools are used to manipulate public opinion by influencing voting decisions. The countries considered in the study are two economic powers of Western Europe, France and Germany, as well as two countries of the European South, which are affected by the economic and financial crisis, Greece and Italy. The countries from Southern Europe were included in the sample as they are often used by mass media as political campaign tools. This paper emphasizes the Twitter platform through which the data collection was implemented using the official API of the social networking tool, focusing on the 2019 European elections. We collected data on 88 party leaders and MEP candidates between 10 May and 30 May 2019, as well as on 44,651 accounts that retweeted them. We concluded using 237,813 election-related tweets and used network theory to analyze and visualize the data. The results demonstrate that all political parties use amplifiers to promote their tweets, and some use the same amplifiers between different countries.

The systematic coverage of the coronavirus pandemic by the Greek mass media began in February 2020, specifically, from the time the virus made its appearance in the most significant way in Italy. Until then, news about the virus had been sporadically visible depending mainly on news reports coming from the international media and press agencies. The assessment of the COVID-19 pandemic as an “infodemic” by the World Health Organization (WHO) made obvious the need to study media coverage and map its patterns, along with the unprecedented political and social response and the massive consequences on the global economy. Through a large content analysis, containing 7457 news items from 13 different media outlets, plus a comparative Twitter analysis of 36,317 tweets, we took the present situation as an opportunity to collect real-time data but also as a point of departure for addressing issues connected to journalistic practices and technological changes in the framework of COVID-19. According to our findings, the Greek media faced the crisis “with a view to the world”, emphasizing international coverage, giving priority to the authorities and scientists, and keeping (at least in their majority) hoaxes and conspiracy theories out of the agenda.

The present paper focuses on adaptive audio detection, segmentation and classification techniques in audio broadcasting content, dedicated mainly to voice data. The suggested framework addresses a real case scenario encountered in media services and especially radio streams, aiming to fulfill diverse (semi-) automated indexing/annotation and management necessities. In this context, aggregated radio content is collected, featuring small input datasets, which are utilized for adaptive classification experiments, without searching, at this point, for a generic pattern recognition solution. Hierarchical and hybrid taxonomies are proposed, firstly to discriminate voice data in radio streams and thereafter to detect single speaker voices, and when this is the case, the experiments proceed into a final layer of gender classification. It is worth mentioning that stand-alone and combined supervised and clustering techniques are tested along with multivariate window tuning, towards the extraction of meaningful results based on overall and partial performance rates. Furthermore, the current work via data augmentation mechanisms contributes to the formulation of a dynamic Generic Audio Classification Repository to be subjected, in the future, to adaptive multilabel experimentation with more sophisticated techniques, such as deep architectures.

Social media platforms have led to the creation of a vast amount of information produced by users and published publicly, facilitating participation in the public sphere, but also giving the opportunity for certain users to publish hateful content. This content mainly involves offensive/discriminative speech towards social groups or individuals (based on racial, religious, gender or other characteristics) and could possibly lead into subsequent hate actions/crimes due to persistent escalation. Content management and moderation in big data volumes can no longer be supported manually. In the current research, a web framework is presented and evaluated for the collection, analysis, and aggregation of multilingual textual content from various online sources. The framework is designed to address the needs of human users, journalists, academics, and the public to collect and analyze content from social media and the web in Spanish, Italian, Greek, and English, without prior training or a background in Computer Science. The backend functionality provides content collection and monitoring, semantic analysis including hate speech detection and sentiment analysis using machine learning models and rule-based algorithms, storing, querying, and retrieving such content along with the relevant metadata in a database. This functionality is assessed through a graphic user interface that is accessed using a web browser. An evaluation procedure was held through online questionnaires, including journalists and students, proving the feasibility of the use of the proposed framework by non-experts for the defined use-case scenarios.

In this article, the quality of media studies education through effective teaching utilizing audiovisual media technologies and audiovisual content (audiovisual media communications) to budding journalists as adult learners (18 years and older) is researched, with results primarily intended for application in radio lessons at all educational levels and disciplines (including adult education). Nowadays, audiovisual media communications play an important role in the modern and visual-centric way of our life, while they require all of us to possess multiple-multimodal skills to have a successful professional practice and career, and especially those who study media studies, such as tomorrow’s new journalists. Data were collected after three interactive teachings with emphasis on educational effectiveness in technology-enhanced learning, through a specially designed written questionnaire with a qualitative and quantitative form (evaluation form), as case study experiments that applied qualitative action research with quasi-experiments. The results (a) confirmed (i) the theory of audiovisual media in education, as well as (ii) the genealogical characteristics and habits of budding journalists as highlighted in basic generational theory, something which appears to be in agreement with findings of previous studies and research; and (b) showed that (i) teaching methodology and educational techniques aimed primarily at adult learners in adult education kept the interest and attention of the budding journalists through the use of such specific educational communication tools as audiovisual media technologies, as well as (ii) sound/audio media, as audiovisual content may hold a significant part in a lecture.

Speech Emotion Recognition (SER) is an important part of Affective Computing and emotionally aware Human–Computer Interaction. Emotional expression may vary depending on the language, culture, and the speaker’s personality and vocal attributes. Speaker-adaptive systems can address this issue. In real-world applications, it is not feasible to obtain big datasets for deep learning model training from a specific speaker. This paper proposes a transfer learning approach for personalized SER based on convolutional neural networks. A CNN is trained in a multi-user dataset for generalization and then is fine-tuned for a small speaker-specific dataset. A VGGish model, pre-trained a large-scale dataset for audio event recognition is also evaluated for the task. This comparison highlights the significance of network capacity, dataset length, and domain-relativity for transfer learning. To enhance the applicability of this approach in real-world conditions, a web crowdsourcing application is implemented. An online platform is provided where contributors can follow a standard procedure to record and submit annotated utterances of emotional speech. The recordings are validated and added to the publicly available AESDD dataset of emotional speech. The platform can be used for the creation of personalized emotional speech datasets for speaker-adaptive SER, following the transfer learning strategies that have been evaluated.

Media authentication relies on the detection of inconsistencies that may indicate malicious editing in audio and video files. Traditionally, authentication processes are performed by forensics professionals using dedicated tools. There is rich research on the automation of this procedure, but the results do not yet guarantee the feasibility of providing automated tools. In the current approach, a computer-supported toolbox is presented, providing online functionality for assisting technically inexperienced users (journalists or the public) to investigate visually the consistency of audio streams. Several algorithms based on previous research have been incorporated on the backend of the proposed system, including a novel CNN model that performs a Signal-to-Reverberation-Ratio (SRR) estimation with a mean square error of 2.9%. The user can access the web application online through a web browser. After providing an audio/video file or a YouTube link, the application returns as output a set of interactive visualizations that can allow the user to investigate the authenticity of the file. The visualizations are generated based on the outcomes of Digital Signal Processing and Machine Learning models. The files are stored in a database, along with their analysis results and annotation. Following a crowdsourcing methodology, users are allowed to contribute by annotating files from the dataset concerning their authenticity. The evaluation version of the web application is publicly available online.

Audiovisual content is highly employed in modern education curricula. This study aimed to identify students’ perceptions regarding the use of video in the courses they attend. Quantitative research was conducted among students of journalism and communication studies. Four courses were used for this study that employ video-based and video-assisted teaching strategies for active learning. Students used videos in different educational settings for flipped learning, blended learning, and autonomous self-paced learning. Their perceptions were examined through an extended form of the Technology Acceptance Model, encompassing more parameters (such as self-efficacy, perceived enjoyment, satisfaction, attitude, and intention of use) to identify causal relationships. In addition, the roles of technical parameters, such as internet connection, audio quality, and video quality, along with the duration of a video, were explored. The findings reveal students’ positive attitudes towards videos, and all factors were significantly related to the intention of use. In addition, the technical aspects of the video did not constitute a problem in the educational process, most likely due to contemporary quality in internet connections and audiovisual productions along with playback equipment. The duration of the videos was also not presented as an issue as there are flexible ways to embed videos in the learning process. Overall, videos can be used in multiple active learning scenarios to enhance students’ motivation and engagement and provide a joyful, collaborative, and hospitable learning environment. Students are familiar with digital tools and technologies and seem to enjoy audiovisual material in the learning process.

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.

The current paper focuses on the investigation of spoken-language classification in audio broadcasting content. The approach reflects a real-word scenario, encountered in modern media/monitoring organizations, where semi-automated indexing/documentation is deployed, which could be facilitated by the proposed language detection preprocessing. Multilingual audio recordings of specific radio streams are formed into a small dataset, which is used for the adaptive classification experiments, without seeking—at this step—for a generic language recognition model. Specifically, hierarchical discrimination schemes are followed to separate voice signals before classifying the spoken languages. Supervised and unsupervised machine learning is utilized at various windowing configurations to test the validity of our hypothesis. Besides the analysis of the achieved recognition scores (partial and overall), late integration models are proposed for semi-automatically annotation of new audio recordings. Hence, data augmentation mechanisms are offered, aiming at gradually formulating a Generic Audio Language Classification Repository. This database constitutes a program-adaptive collection that, beside the self-indexing metadata mechanisms, could facilitate generic language classification models in the future, through state-of-art techniques like deep learning. This approach matches the investigatory inception of the project, which seeks for indicators that could be applied in a second step with a larger dataset and/or an already pre-trained model, with the purpose to deliver overall results.

Art and technology have always been very tightly intertwined, presenting strong influences on each other. On the other hand, technological evolution led to today’s digital media landscape, elaborating mediated communication tools, thus providing new creative means of expression (i.e., new-media art). Rich-media interaction can expedite the whole process into an augmented schooling experience though art cannot be easily enclosed in classical teaching procedures. The current work focuses on the deployment of a modern-art web-guide, aiming at enhancing traditional approaches with machine-assisted blended-learning. In this perspective, “machine” has a two-folded goal: to offer highly-interdisciplinary multimedia services for both in-class demonstration and self-training support, and to crowdsource users’ feedback, as to train artificial intelligence systems on painting movements semantics. The paper presents the implementation of the “Istoriart” website through the main phases of Analysis, Design, Development, and Evaluation, while also answering typical questions regarding its impact on the targeted audience. Hence, elaborating on this constructive case study, initial hypotheses on the multidisciplinary usefulness, and contribution of the new digital services are put into test and verified.

The internet and web technologies have radically changed the way that users interact with museum exhibits. The websites and their related services play an important role in accessibility and interaction with the multimedia content of museums. The aim of the current research is to present a heuristic evaluation of forty-seven medical and anatomy museum websites from usability experts, for the determination of the principal/key characteristics and issues towards the effective design of a museum website. For homogeneity and comparison purposes, the websites of museums with no support of English language were not included in the evaluation process. In the present paper, the methodology was structured with the assessment of the technologies and services of anatomy museum websites in mind. The results of the current statistical examination are subsequently analyzed and discussed.

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.

Journalism is in a state of perpetual change due to the implementation of technological tools on an everyday basis. In the contemporary news distribution environment, the boundaries between media are no longer distinct, therefore media/journalism educational institutions should adapt curricula according to the needs of each type of medium. The aim of this paper is to present the incorporated technologies in the radio journalism undergraduate course of the School of Journalism and Mass Communications at the Aristotle University of Thessaloniki and evaluate their necessity according to the students themselves. After the completion of the course, which involves the use of specialized technological tools in audio recording and editing, a survey employing quantitative method based on the technology acceptance model was conducted. The results indicate that the students, despite the difficulties that were encountered due to the initial lack of knowledge on the technology applied, evaluated very highly both their satisfaction with the produced outcome and the usefulness of the procedure. In fact, the major result is that perceived usefulness is a key factor that determines the utilization of specialized technology.