Fake Content Research Articles

Socio-political hoaxes in virtual public space: Assertive illocutionary manifestations of dishonesty in a critical pragmatics perspective

Fake news in a socio-political context is a form of language abuse that aims to create chaos and influence public opinion wrongly. The purpose of this study is (1) to describe the purpose of socio-political hoaxes in the public space as a clear manifestation of dishonesty. (2) Describe the purpose of socio-political hoaxes in the public space as a clear manifestation of dishonesty. This study applies a qualitative descriptive approach to explore the complexity of language phenomena in the context of socio-political hoaxes. The research data is a snippet of a speech of socio-political reality on social media. The data collection method is a listening method that involves reading and recording social media content. In the analysis stage, additional language matching methods are applied to connect the language used with the relevant social, cultural, and situational context. The analysis steps carried out include identification, classification, typification, and interpretation. The findings of the embodiment of the form and purpose of this research are presented as follows: (1) Fake content with the aim of provoking and misleading the public; (2) Fake video content with the purpose of misleading and lying; (3) False illustrations with the aim of deceiving the public; (4) False content with the purpose of defaming and deceiving the public; (5) False content with the purpose of inciting and misleading the public; (6) Hoaxes with the purpose of insulting and defaming; (7) False content with the aim of inciting and misleading the public.

Concerns related to new information tool-synthetic media and Deep Fake content

Media content, form, and technology innovation are all being significantly impacted by artificial intelligence. These days, artificial intelligence is incorporated into every news media program to some extent. In particular, improvements in character-based artificial intelligence began to assist journalists in their daily work. On the other hand, artificial intelligence has given rise to a whole new kind of misleading information that is virtually identical to actual information in both form and structure. Synthetic media and Deep Fake information are terms used to describe false information and news produced with artificial intelligence. Artificial intelligence that creates deep fake content uses the three main methods of machine learning—supervised, unsupervised, and reinforcement learning—as well as the technology referred to as Generative Adversarial Networks (GANs), which produce fake images that resemble face swaps or real-world video simulations and can identify and learn from deep fake information. They have also advanced to a point where they can create convincingly fake videos on their own without assistance from humans, which will have a big impact on the entertainment, media, and film sectors. Therefore, three things need to be done to stop this kind of information from spreading: firstly, create an algorithm with countermeasures; secondly, teach journalists and editors to double-check the information to make sure it makes sense; and thirdly, public media education must be enhanced. If we do nothing about deep fake content right now, people will stop believing in news sources completely, and the infocalypse is quickly approaching. To be more precise, several studies have revealed that the people in our country lack media literacy. Consequently, if this kind of information reaches us, it will be quite challenging to control and halt it. Мэдээллийн шинэ хэрэгсэл дэх синтетик медиа буюу гүн худал (synthetic media-Deep Fake) контентийн асуудалд Хураангуй. Хиймэл оюун ухааны ололт амжилтууд хэвлэл мэдээллийн хэрэгслүүдэд цөөнгүй дэвшилийг авчирчээ. Гэвч үүнийг дагаад шинэ технологийг ашигласан синтетик медиа, гүн худал контентийн асуудал бий болж, үүнтэй тэмцэх, сэргийлэх явдал дэлхий дахины хэвлэл мэдээлэл, олон нийтийн харилцааны салбарын мэргэжилтнүүдийн хувьд нэн чухал асуудал болов. Гүн худал мэдээлэл нь L(μG,μD)≔EX~y~μD(x)[In y]+EX~y~μD(x)[In(1-y)]. математик тэгшитгэлийн дагуу бэлтгэгдэж байна. Мөн алгоритм нь хиймэл оюун ухааны машин сургалтын аргын зэрэгцээ бодит хүний видео симуляци эсвэл нүүр царайг солих байдлаар хуурамч дүрсийг боловсруулдаг, зураг бичлэгийг зохиомлоор бүтээдэг, гүн хуурамч контентын алдааг олж, түүн дээрээ суралцдаг Generative Adversarial Networks технологид суурилан хөгжих болсноор хэн нэгний оролцоогүйгээр бие даан худал видео дүрслэлийг бэлтгэх ур чадвартай болжээ. Иймээс гүн хуурамч контенттой эртхэн тэмцэж эхлэхгүй бол иргэдийн мэдээ мэдээлэлд итгэх итгэл нэг мөсөн буурах цаашлаад мэдээллийн сүйрэл (infocalypse)-ыг бий болгоход ч ойрхон байна.

Analisis Pengaturan Hukum Pidana terhadap Penyalahgunaan Teknologi Manipulasi Gambar (Deepfake) dalam Penyebaran Konten Pornografi Melalui Akun Media Sosial

Deepfake is an advanced technology that utilizes the latest artificial intelligence techniques to produce compelling images or motion pictures that appear to be genuine. This technique uses specialized algorithms to create fake content that is difficult to distinguish from the real thing. In Indonesia, cases of deepfake pornography have increased in recent years. With this new Artificial Intelligence technology which in addition to the many positive impacts it provides, it also has negative impacts that can support the occurrence of these cases. the spread of pornographic content with image manipulation (deepfake) has become one of the negative impacts of this technology. This study aims to determine the shortcomings in the legal arrangements and how they are implemented in cases of deepfake, or the misuse of AI technology. The research method used is normative legal research based on literature and laws and regulations. This research found that several general and specific rules relating to the misuse of deepfake technology through social media have not covered all aspects of image manipulation (deepfake) containing pornography on social media accounts. Therefore, although pornographic deepfake cases often occur and harm some people, the limited legal regulation of these acts causes the legal apparatus to have difficulty in ensnaring the perpetrators.

About Some Socio-economic Problems and Risks of Artificial Intelligence

Article analyses some socio-economic risks related to application of artificial intelligence (AI) in several fields of activity. Also, existing gaps in legal regulation of activities related to artificial intelligence are investigated. Article clarifies issues related to determining the division of liability for certain legal consequences resulting from artificial intelligence activity. Also, norms and principles to be adhered to in order to protect personal data during application of AI are demonstrated. As one of the concerns among people regarding artificial intelligence, article notes the importance of provision of transparence and accountability of this technology. Simultaneously, article interprets problems arising from relations of artificial intelligence and intellectual property, as well as recognition of property rights for intellectual products created via AI. Also, macro and micro-level impact of artificial intelligence on economy is analyzed. Attention is paid to issues such as productivity, competition, changes in the nature of the labor market, the increase in unemployment, and the deepening of social and digital inequality as a result of the application of this technology. Moreover, advantages and risks of human-robot collaboration are evaluated. Article demonstrates the biggest threats of artificial intelligence – creation of fake content, misinformation and hence, creation of significant problems. Prevention methods of those threats are interpreted on technological and legal planes. Also, risks of application of artificial intelligence in critical fields such as military and health are characterized.

Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks

As facial modification technology advances rapidly, it poses a challenge to methods used to detect fake faces. The advent of deep learning and AI-based technologies has led to the creation of counterfeit photographs that are more difficult to discern apart from real ones. Existing Deep fake detection systems excel at spotting fake content with low visual quality and are easily recognized by visual artifacts. The study employed a unique active forensic strategy Compact Ensemble-based discriminators architecture using Deep Conditional Generative Adversarial Networks (CED-DCGAN), for identifying real-time deep fakes in video conferencing. DCGAN focuses on video-deep fake detection on features since technologies for creating convincing fakes are improving rapidly. As a first step towards recognizing DCGAN-generated images, split real-time video images into frames containing essential elements and then use that bandwidth to train an ensemble-based discriminator as a classifier. Spectra anomalies are produced by up-sampling processes, standard procedures in GAN systems for making large amounts of fake data films. The Compact Ensemble discriminator (CED) concentrates on the most distinguishing feature between the natural and synthetic images, giving the generators a robust training signal. As empirical results on publicly available datasets show, the suggested algorithms outperform state-of-the-art methods and the proposed CED-DCGAN technique successfully detects high-fidelity deep fakes in video conferencing and generalizes well when comparing with other techniques. Python tool is used for implementing this proposed study and the accuracy obtained for proposed work is 98.23 %.

A Systematic Review on Deep Fake Image Generation, Detection Techniques, Ethical Implications, and Overcoming Challenges

Deepfake technology, rooted in sophisticated machine learning techniques, utilizes deep neural networks to create highly realistic fake content such as videos, audio recordings, and images. This technology has rapidly evolved due to advancements in deep learning models, computational power, and data availability. The ethical implications, social impact, misuse, and legal frameworks surrounding Deepfake technology have been extensively studied. Detection techniques using deep learning approaches have been developed to combat the challenges posed by Deepfake content. Recommendations for future research include enhancing detection techniques, integrating explainable AI, and exploring real-time detection systems. Industry and policy implications emphasize the need for robust detection technologies, comprehensive legal frameworks, and collaborative efforts to address ethical concerns and regulate Deepfake content. This systematic review explores the landscape of deep fake image generation, detection techniques and challenges, in addition to ethical considerations. By synthesizing existing research, we aim to provide insights into deep-fake technology's advancements, limitations, and societal implications. This review underscores the urgent need for interdisciplinary collaboration and robust frameworks to address the multifaceted issues surrounding deep fakes in the digital age.

A Survey an Optimized Dense CNN Model for Recognizing Deepfake Images

Deepfake detection relies on a deep learning model. Deepfake content is generated using artificial intelligence and machine learning techniques to swap one person’s face with another’s in images. These altered images are certain to have a significant impact on society. Deepfakes utilize advanced technologies such as machine learning (ML) and deep learning (DL) to develop automated techniques for generating deceptive content. The model is trained using the DFDC (Deepfake Detection Challenge) dataset, which includes 1, 00,000 videos comprising both real and fake content. This survey examines recent progress in deep learning techniques for detecting deepfake images, highlighting their growing significance in today’s digital landscape. The paper outlines the issues arising from deepfakes, explores the techniques for generating deceptive content, and analyzes various deep learning models employed in deepfake detection, such as convolutional neural networks (CNN’s), MTCNN (Multi-task CNN), and Facial Landmark. It emphasizes the ongoing battle between the progression of deepfakes and the evolution of detection methods, underscoring the need for advanced and flexible neural network architectures to effectively curb the dissemination of deceptive information.

A Survey on the Use of Large Language Models (LLMs) in Fake News

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall short in the face of increasingly sophisticated fake content. This review article explores the emerging role of Large Language Models (LLMs) in enhancing the detection of fake news and fake profiles. We provide a comprehensive overview of the nature and spread of disinformation, followed by an examination of existing detection methodologies. The article delves into the capabilities of LLMs in generating both fake news and fake profiles, highlighting their dual role as both a tool for disinformation and a powerful means of detection. We discuss the various applications of LLMs in text classification, fact-checking, verification, and contextual analysis, demonstrating how these models surpass traditional methods in accuracy and efficiency. Additionally, the article covers LLM-based detection of fake profiles through profile attribute analysis, network analysis, and behavior pattern recognition. Through comparative analysis, we showcase the advantages of LLMs over conventional techniques and present case studies that illustrate practical applications. Despite their potential, LLMs face challenges such as computational demands and ethical concerns, which we discuss in more detail. The review concludes with future directions for research and development in LLM-based fake news and fake profile detection, underscoring the importance of continued innovation to safeguard the authenticity of online information.

Are Psychopathy Traits Related to the Use of Artificial Intelligence Tools Among University Students? The Mediating Effect of Self-control

ABSTRACT Based on the “brakes” model of self-control, this study sought to analyze if psychopathy traits are related to Artificial Intelligence use, and whether low levels of self-control mediate this hypothetical relation. A cross-sectional study involving 1687 undergraduate students was conducted. Psychopathy traits predicted more use of artificial intelligence, as well as using these tools to do academic work instead of the student and to create fake content. However, self-control just mediated the link between psychopathy traits and the frequency of Artificial Intelligence use, suggesting that the misuse of Artificial Intelligence tools might be planned rather than impulsive behavior.

Faking the war: fake posts on Turkish social media during the Russia–Ukraine war

This study aims to analyze the fake posts circulated on Turkish social media during the Russia–Ukraine war. With advancing technology, social media platforms have a profound impact on the way we perceive and interpret events and make us question the accuracy of information generated about international events such as wars. While the Russia–Ukraine war constitutes an important turning point in international relations, the reflection of these events on social media is also seen in fake posts. In this context, the main purpose of this study is to identify the common themes of fake social media posts and to reveal the general context of these posts on social media. In addition, the study aims to analyze the fake content circulating on Turkish social media and to reveal the emerging polarized discourses through the identified themes. The research revolves around five main themes that feed polarization: war reporting, ideological misrepresentation, humor, hate speech, and conspiracy theories. The findings show that fake content is particularly concentrated around ideological polarization and antagonisms. It was also found that misinformation and decontextualized humor blurred the true context of the war and that fake content combined with hate speech and conspiracy theories distorted the context of the war.

AI Integrity Solutions for Deepfake Identification and Prevention

The increasing complexity of deepfake technology has sparked significant worries over individual privacy, the spread of false information, and deficiencies in cybersecurity. Deepfakes have the ability to effectively modify audio and visual content, resulting in a growing challenge to differentiate between real and fake content. To address this critical challenge, the study is conducting a survey to reveal a broad range of perspectives on the familiarity, encounters, and concerns related to deepfake technology. In addition, the study evaluates the effectiveness of current strategies in addressing the spread of deepfake material and proposes future approaches for improving the integrity of AI. The survey was delivered digitally, and responses were examined to provide an in- depth analysis of the latest techniques and difficulties in the context of deepfake detection. The outcomes demonstrate a range of perspectives on understanding deepfakes with an explicit agreement on the importance of risk presented by harmful deepfake applications. Although the participants showed an understanding of the available interventions, they also identified considerable challenges and the need for improved awareness, robust detection tools, and ethical standards in AI development to address the challenges posed by deepfakes with the present detection method. Implementing AI ethical guidelines to avoid deepfakes has a significant and beneficial effect on several industries by providing protection against their harmful effects. By fostering AI ethical guidelines, these policies are able to foster societal trust, mitigate risk, and cultivate a more robust digital environment.

Artificial Intelligence for Media: Opportunities or Threats

The emergence of artificial intelligence (AI) has ushered in an era of unprecedented possibilities and, at the same time, poses significant threats in the media landscape. As a potent tool, AI can streamline operations, offering innovative solutions like content personalization, predictive analytics, and augmented reality experiences that can enhance user engagement and boost the media industry’s economic viability. These opportunities allow for a more immersive and personalized user experience, leveraging data to create content that is finely tuned to individual preferences and trends. However, the integration of AI in media does not come without its challenges. The threat of deep fakes and the dissemination of misinformation stand as substantial concerns, with AI technologies facilitating the creation of highly convincing fake content that can manipulate perceptions and sow discord. Moreover, the autonomy given to AI systems can potentially lead to job displacements and raise ethical concerns regarding privacy and data security. Despite these threats, when wielded responsibly and ethically, AI can usher in a new golden age for media characterized by creativity, efficiency, and innovation. As the media industry stands at this crossroads, it must navigate the fine balance between leveraging AI’s opportunities and mitigating its potential threats, fostering a landscape that upholds truth, ethics, and human value at its core. It is imperative for stakeholders to collaborate, devising strategies and regulatory frameworks to ensure AI serves as a force for good, steering media towards a future that embodies progress and inclusivity.

Detection of fake face images using lightweight convolutional neural networks with stacking ensemble learning method.

Images and videos containing fake faces are the most common type of digital manipulation. Such content can lead to negative consequences by spreading false information. The use of machine learning algorithms to produce fake face images has made it challenging to distinguish between genuine and fake content. Face manipulations are categorized into four basic groups: entire face synthesis, face identity manipulation (deepfake), facial attribute manipulation and facial expression manipulation. The study utilized lightweight convolutional neural networks to detect fake face images generated by using entire face synthesis and generative adversarial networks. The dataset used in the training process includes 70,000 real images in the FFHQ dataset and 70,000 fake images produced with StyleGAN2 using the FFHQ dataset. 80% of the dataset was used for training and 20% for testing. Initially, the MobileNet, MobileNetV2, EfficientNetB0, and NASNetMobile convolutional neural networks were trained separately for the training process. In the training, the models were pre-trained on ImageNet and reused with transfer learning. As a result of the first trainings EfficientNetB0 algorithm reached the highest accuracy of 93.64%. The EfficientNetB0 algorithm was revised to increase its accuracy rate by adding two dense layers (256 neurons) with ReLU activation, two dropout layers, one flattening layer, one dense layer (128 neurons) with ReLU activation function, and a softmax activation function used for the classification dense layer with two nodes. As a result of this process accuracy rate of 95.48% was achieved with EfficientNetB0 algorithm. Finally, the model that achieved 95.48% accuracy was used to train MobileNet and MobileNetV2 models together using the stacking ensemble learning method, resulting in the highest accuracy rate of 96.44%.

FROM MEDIA LITERACY TO INDIVIDUAL INFORMATION SECURITY OF UNIVERSITY STUDENTS

The authors focus on developing the individual information security skills of higher education students due to the intensification of students’ interaction with different types and forms of fake content. The paper theoretically substantiates the developed pedagogical technology and presents educational and methodical developments. The authors present the design and results of testing (Module 1,2 of the technology) with the participation of undergraduate students at the Siberian Institute of Management RANEPA (N = 147), The testing was conducted as a comprehensive verification based on the authors’ classification criteria, selected set of technical means, and recommendations for identifying cognitive distortions.

Large language models debunk fake and sensational wildlife news

AbstractIn the current era of rapid online information growth, distinguishing facts from sensationalized or fake content is a major challenge. Here, we explore the potential of large language models as a tool to fact‐check fake news and sensationalized content about animals. We queried the most popular large language models (ChatGPT 3.5 and 4, and Microsoft Bing), asking them to quantify the likelihood of 14 wildlife groups, often portrayed as dangerous or sensationalized, killing humans or livestock. We then compared these scores with the “real” risk obtained from relevant literature and/or expert opinion. We found a positive relationship between the likelihood risk score obtained from large language models and the “real” risk. This indicates the promising potential of large language models in fact‐checking information about commonly misrepresented and widely feared animals, including jellyfish, wasps, spiders, vultures, and various large carnivores. Our analysis underscores the crucial role of large language models in dispelling wildlife myths, helping to mitigate human–wildlife conflicts, shaping a more just and harmonious coexistence, and ultimately aiding biological conservation.

Securing brand trust and reputation in an era of political polarisation and AI-driven disinformation

This paper analyses the seismic reputational risks now facing brands as two major sociotechnical forces converge — the rise of ideological polarisation and the emergence of AI’s ability to generate synthetic media impersonating organisations. It examines how brands have embraced polarised stances on social issues to build deeper connections with values-driven consumers, especially younger demographics like millennials and Gen Z. By adopting partisan positions. Brands also become targets for ideological attacks seeking to erode trust. Meanwhile, rapid advances in generative AI empower malicious actors to disseminate disinformation, attacking brands with harmful narratives, misinformation and disinformation on an unprecedented scale and realism to do financial and reputational harm. The paper provides an in-depth examination of both dynamics. It explores how younger consumers increasingly demand corporate advocacy on divisive issues, forcing brands to navigate complex sociopolitical fault lines. Taking progressive stances risks a backlash from ideological opponents ready to instigate reputation-damaging retaliation. The paper also details the exponential threats introduced by AI synthesis technologies like generative adversarial networks. It explains how even modestly resourced attackers could soon produce unlimited volumes of fake content, misinformation and disinformation to target brands across all digital channels and media types. To address these converging risks, the paper recommends that brands invest in capabilities including monitoring sociocultural tensions, technologically authenticating communications, establishing early disinformation warning systems, educating consumers on resisting manipulation, crafting proactive narratives, practicing radical transparency and testing defences. It emphasises the need for holistic ‘narrative intelligence’ combining data analysis, cultural discernment and human wisdom. With deliberate strategies focused on verification, transparency and continuous learning, brands can foster resilience and enduring trust despite inevitable disruptions. The paper concludes that competence in navigating partisan debates and AI deception will become a competitive advantage in determining which brands lead in this complex future.

Deepfake video detection: challenges and opportunities

Deepfake videos are a growing social issue. These videos are manipulated by artificial intelligence (AI) techniques (especially deep learning), an emerging societal issue. Malicious individuals misuse deepfake technologies to spread false information, such as fake images, videos, and audio. The development of convincing fake content threatens politics, security, and privacy. The majority of deepfake video detection methods are data-driven. This survey paper aims to thoroughly analyse deepfake video generation and detection. The paper’s main contribution is the classification of the many challenges encountered while detecting deepfake videos. The paper discusses data challenges such as unbalanced datasets and inadequate labelled training data. Training challenges include the need for many computational resources. It also addresses reliability challenges, including overconfidence in detection methods and emerging manipulation approaches. The research emphasises the dominance of deep learning-based methods in detecting deepfakes despite their computational efficiency and generalisation limitations. However, it also acknowledges the drawbacks of these approaches, such as their limited computing efficiency and generalisation. The research also critically evaluates deepfake datasets, emphasising the necessity for good-quality datasets to improve detection methods. The study also indicates major research gaps, guiding future deepfake detection research. This entails developing robust models for real-time detection.

Anatomy of an AI-powered malicious social botnet

Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far remains anecdotal. This paper presents a case study about a Twitter botnet that appears to employ ChatGPT to generate human-like content. Through heuristics, we identify 1,140 accounts and validate them via manual annotation. These accounts form a dense cluster of fake personas that exhibit similar behaviors, including posting machine-generated content and stolen images, and engage with each other through replies and retweets. ChatGPT-generated content promotes suspicious websites and spreads harmful comments. While the accounts in the AI botnet can be detected through their coordination patterns, current state-of-the-art LLM content classifiers fail to discriminate between them and human accounts in the wild. These findings highlight the threats posed by AI-enabled social bots.

Disinformation and health: fact-checking strategies of Spanish health public institutions through YouTube

IntroductionThrough their social media, public institutions address fake news and tackle disinformation that affects both them and the general public. With the rise of online video platforms, the audiovisual format has made its way as a dynamic and engaging format that allows for the creation of narratives to counter distorted information. This circumstance is particularly noticeable in the healthcare sector, where most of the recent fact-checking activity is taking place.MethodologyThrough the analysis of the official YouTube channels of the health departments of all Spanish autonomous communities from 1 January 2021 to 31 December 2023, this paper explores the use of audiovisual fact-checking by Spanish regional governments, and it describes the nature of these videos and the strategies undertaken on that platform to tackle disinformation.ResultsDespite the expected territorial disparities, the research findings show that an increasing number of Spanish regional health authorities are using YouTube to fight disinformation. They also show that the videos posted on that platform provide a strong response to fake news, either as a preventive measure or in response to existing publications.DiscussionIn the current landscape of declining mass media, where audience dispersion leads a to (sometimes deliberate) fragmentation of discourse, we are confronted with the paradox of being a society with access to a vast amount of information, but not well-informed. Therefore, it is essential to have strong institutions that verify fake content through popular formats.

“What Scares Me Is the Speed at Which Artificial Intelligence Is Developing”: Students' Perceptions of Artificial Intelligence in Foreign Language Teaching

As artificial intelligence (AI) becomes an integral part of our daily lives, the concern of the teaching community about the illegal use of these technologies in the educational process is increasing. In order to adapt the education system and teaching practices to new technological challenges, it is necessary to analyze the opinions of all the parties concerned. The purpose of this study is to identify the attitude of students of Kazan Federal University to the use of artificial intelligence technologies in the educational process and the practice of their application in foreign languages learning process. To achieve this goal, an online survey of students of Kazan Federal University was conducted. The survey touched upon the practical aspects of the use of artificial intelligence in language teaching, the advantages and disadvantages of AI tools from the students’ point of view, as well as their opinion regarding the prospects of AI in education. As a result of the study, we came to the conclusion that at the moment AI tools are not widespread enough in teaching foreign languages. Only one-fifth of the respondents use these tools, but the respondents’ comments suggest that the number of users will grow. The attitude of students towards the use of AI is ambiguous, with responses ranging from enthusiastic to skeptical. Students’ positive impressions are mainly related to saving time and effort, as well as to the ability of AI to present complex materials with understandable language. Among the main disadvantages, the respondents noted unreliability of data and fake content. Despite the fact that students are generally positive about the use of AI, a significant part of respondents do not trust software products such as ChatGPT, since, in their opinion, it provides answers of average quality that need to be corrected. Based on the data obtained, the authors attempted to formulate recommendations on improving the methods of teaching and control in the process of teaching foreign languages at universities.