Dissemination Of Information Research Articles

Racial and Demographic Disparities in Susceptibility to Health Misinformation on Social Media: National Survey-Based Analysis.

Social media platforms have transformed the dissemination of health information, allowing for rapid and widespread sharing of content. However, alongside valuable medical knowledge, these platforms have also become channels for the spread of health misinformation, including false claims and misleading advice, which can lead to significant public health risks. Susceptibility to health misinformation varies and is influenced by individuals' cultural, social, and personal backgrounds, further complicating efforts to combat its spread. This study aimed to examine the extent to which individuals report encountering health-related misinformation on social media and to assess how racial, ethnic, and sociodemographic factors influence susceptibility to such misinformation. Data from the Health Information National Trends Survey (HINTS; Cycle 6), conducted by the National Cancer Institute with 5041 US adults between March and November 2022, was used to explore associations between racial and sociodemographic factors (age, gender, race/ethnicity, annual household income, marital status, and location) and susceptibility variables, including encounters with misleading health information on social media, difficulty in assessing information truthfulness, discussions with health providers, and making health decisions based on such information. Over 35.61% (1740/4959) of respondents reported encountering "a lot" of misleading health information on social media, with an additional 45% (2256/4959) reporting seeing "some" amount of health misinformation. Racial disparities were evident in comparison with Whites, with non-Hispanic Black (odds ratio [OR] 0.45, 95% CI 0.33-0.6, P<.01) and Hispanic (OR 0.54, 95% CI 0.41-0.71, P<.01) individuals reporting lower odds of finding deceptive information, while Hispanic (OR 1.68, 95% CI 1.48-1.98, P<.05) and non-Hispanic Asian (OR 1.96, 95% CI 1.21-3.18, P<.01) individuals exhibited higher odds in having difficulties in assessing the veracity of health information found on social media. Hispanic and Asian individuals were more likely to discuss with providers and make health decisions based on social media information. Older adults aged ≥75 years exhibited challenges in assessing health information on social media (OR 0.63, 95% CI 0.43-0.93, P<.01), while younger adults (18-34) showed increased vulnerability to health misinformation. In addition, income levels were linked to higher exposure to health misinformation on social media: individuals with annual household incomes between US $50,000 and US $75,000 (OR 1.74, 95% CI 1.14-2.68, P<.01), and greater than US $75,000 (OR 1.78, 95% CI 1.20-2.66, P<.01) exhibited greater odds, revealing complexities in decision-making and information access. This study highlights the pervasive presence of health misinformation on social media, revealing vulnerabilities across racial, age, and income groups, underscoring the need for tailored interventions.

Dissemination in Social Media and Blogs of Public Health Information and Misinformation on Covid-19 Containment in Switzerland

Future epidemics are perceived as inevitable. Dissemination of information can enhance awareness, serving as an initial stride towards fostering desired epidemic-controlling actions among the public. In this study, a qualitative content analysis of Covid-19- and Switzerland-related social media and blog contributions points at a limited adoption of public health key messages and a negative reputation of the informing authorities. The authorities are to a marginal extent the source of information and a controversial sentiment towards vaccination emerges. In addition, we find a large share of disseminated information that is not conducive to pandemic containment. Within this, a substantial volume of misinformation emerges in statements on Covid-19-related issues. The misinformation consists primarily of unsubstantiated health consequences of the Covid-19 vaccination (both efficacy and side effects), and, less often, of trivialisation or denial of the pandemic. Furthermore, in a phase of political campaigning on a Covid-law referendum in Switzerland, social media contributors often portray pandemic containment as an undue, unlawful, or autocratic imposition on individual and collective freedom, and as a tool deployed for political repression. In addition, the pandemic or its containment are embedded in various conspiracies by users and containment measures are contested with religious, naturopathic or esoteric arguments.

Managing the road safety risks of last mile deliveries: Do telematics have a role to play?

The research aimed to understand the impact of telematics in reducing traffic violations and crashes among drivers working in parcel delivery and the perceived utility, effectiveness, and acceptability of telematics among drivers using them. The objective was to carry out an online survey among drivers who have telematics versus drivers who do not and compare their behaviors in terms of risks and violations controlling for key demographics an exposure. An online anonymous survey was conducted of 780 home delivery drivers of which 430 used telematics in 2022. The survey was conducted by a fieldwork company and participants were compensated for their time. Univariate and multivariate analysis was conducted on the data. Telematics users did not report less crashes than drivers who did not use telematics. Most drivers, irrespective of telematics use agreed that the time pressure of delivery work increased speed limit violations and unsafe or hazardous parking. Multivariate analysis indicated that damage collisions for all drivers were associated with factors such as, driving medium to large vans, hazardous behavior related to parking, and having penalties for violations, especially related to speed. Delivery drivers, while generally facing pressure to speed and take risks to meet delivery schedules, did perceive telematics as a tool for safety enhancement and information dissemination, as well as for themanagement of performance. Non-telematics users viewed it with skepticism. Telematics alone may not ensure the safety of last-mile deliveries and could potentially increase the pressure of deliveries. This research underscores the need for a holistic approach to road safety in the home delivery sector, combining telematics technology with a proactive safety culture.

Quantum Cryptography and Blockchain-Based Social Media Platforms as a Dual Approach to Securing Financial Transactions in CBDCs and Combating Misinformation in U.S. Elections

This paper explores the integration of quantum cryptography and blockchain technology to address two pressing challenges: securing financial transactions in Central Bank Digital Currencies (CBDCs) and combating the spread of misinformation during U.S. elections through decentralized social media platforms. As quantum computing advances, traditional encryption methods may become obsolete, posing significant risks to digital financial systems. Quantum cryptography, with its quantum-resistant algorithms, offers enhanced protection for CBDC transactions, ensuring long-term security and privacy. Simultaneously, blockchain-based social media platforms provide a decentralized structure that can prevent the dissemination of false information by ensuring transparency and authenticity through cryptographic verification and consensus mechanisms. These platforms also facilitate decentralized identity management, empowering users to verify content without relying on centralized authorities. By combining quantum cryptography’s secure framework with blockchain’s decentralized transparency, this dual approach creates a more secure digital ecosystem that not only safeguards financial transactions but also strengthens democratic processes. The paper further addresses the regulatory and technical challenges associated with implementing these technologies and their potential to shape a more secure, transparent, and accountable future.

COVID-19 vaccine uptake and associated factors among individuals living in a peri-urban area in Uganda: A cross-sectional study.

The Corona virus disease (COVID-19) is a respiratory illness that is caused by SARS-CoV-2 virus. This virus was first reported in China in December 2019. It then spread to all countries and from March 11, 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. In Uganda, the disease was first reported in March 2020 and COVID-19 vaccines became available by January 2021. Although COVID-19 vaccines were available in Uganda, uptake remained low. The aim of this study was to establish COVID-19 vaccine uptake awareness in a peri-urban setting in Entebbe City, Uganda. This was a cross-sectional study conducted among 127 men and 263 women who reside in Entebbe City, Uganda. Data was collected on socio-behavioral characteristics, knowledge, attitude, and practice (KAP) about COVID-19 vaccine using interviewer administered questionnaires. Uptake of COVID-19 vaccine was defined as the proportion of participants who had received at least one dose of the COVID-19 vaccine. We used descriptive statistics to estimate awareness of COVID-19 vaccines. The 'chi-square test' and 'modified Poisson regression' were used to assess variations in uptake of COVID-19 vaccines among respondents and their socio-demographics as well as other characteristics. Ninety-nine percent (388 out of 390) of the study population were aware of at least one brand of COVID-19 vaccines in the country. Thirty-five percent (138 out of 390) knew that the vaccine immunity was achieved 14 days after the 2nd dose and 98.7% (385 out of 390) admitted that observing the standard operating procedure for COVID-19 infection prevention was necessary after vaccination. There was a gap in knowledge on vaccine safety reported by 74.6% (291 out of 390) participants. Some participants 37.2% (145 out of 390) had concerns about the vaccine. Of these, 57.9% (84 out of 145) believed that the vaccines were not helpful; and 30.3% (44 out of 145) feared serious side effects. Sixty-six percent (257 out of 390) believed that vaccines were not working and 79.0% (308 out of 390) admitted that vaccines were promoted for financial gain. At the time of performing the study, 36.2% and 22.3% had received the 1st and 2nd dose respectively. The main sources of information on COVID-19 vaccine were television (TV) and social media (p-value 0.001). In a multivariate model, COVID-19 vaccine acceptability was associated with salaried and self-employment (p-value 0.046). The other predicative factors were awareness of the COVID-19 vaccine (p-value <0.001) and having vaccine concerns (p-value 0.013). Uptake of COVID-19 vaccination in Entebbe community was low, partly attributed to knowledge gaps and concerns about vaccine safety and effectiveness. This highlights the need to enhance dissemination of information about COVID-19 vaccine. The lessons learnt in this study would be relevant for other emerging infections by informing vaccination implementation programs in similar settings.

How user language affects conflict fatality estimates in ChatGPT

OpenAI’s ChatGPT language model has gained popularity as a powerful tool for problem-solving and information retrieval. However, concerns arise about the reproduction of biases present in the language-specific training data. In this study, we address this issue in the context of the Israeli–Palestinian and Turkish–Kurdish conflicts. Using GPT-3.5, we employed an automated query procedure to inquire about casualties in specific airstrikes, in both Hebrew and Arabic for the former conflict and Turkish and Kurdish for the latter. Our analysis reveals that GPT-3.5 provides 34 ± 11% lower fatality estimates when queried in the language of the attacker than in the language of the targeted group. Evasive answers denying the existence of such attacks further increase the discrepancy. A simplified analysis on the current GPT-4 model shows the same trends. To explain the origin of the bias, we conducted a systematic media content analysis of Arabic news sources. The media analysis suggests that the large-language model fails to link specific attacks to the corresponding fatality numbers reported in the Arabic news. Due to its reliance on co-occurring words, the large-language model may provide death tolls from different attacks with greater news impact or cumulative death counts that are prevalent in the training data. Given that large-language models may shape information dissemination in the future, the language bias identified in our study has the potential to amplify existing biases along linguistic dyads and contribute to information bubbles.

Barriers and facilitators to antenatal care services utilisation in Somaliland: a qualitative study.

To explore the barriers and facilitators of antenatal care (ANC) utilisation in Somaliland. An exploratory qualitative study design. Various settings were considered for study including maternal and child health clinics, hospitals and community. 74 study participants including 33 pregnant women, 22 healthcare providers, 10 community elders and ten religious leaders were purposively selected. Data was collected via focus group discussion and in-depth interviews in the local Somali language. The data was audio recorded, transcribed verbatim, translated into English and imported into NVivo V.12 software. Thematic framework analysis was used to assess barriers and facilitators of ANC utilisation. Six main themes were identified as barriers of ANC utilisation. These include lack of awareness, long distance and lack of transportation access, economic barriers, lack of social support, beliefs and thoughts and health facility-related factors such as caregivers approach, waiting time and inadequate skill of providers. Proper dissemination of ANC information, adequate spousal support and cheap pricing of services were found to be facilitators of ANC uptake. A strategy that focuses on raising awareness about ANC, making it accessible by reducing the cost-of-service charge, encouraging partner involvement, improving the quality of services and making it customer-friendly will help to overcome the barriers of antenatal utilisation.

Analysis of the Correlation of Topic Feature Changes Based on the LDA Model

Abstract. As is well known, with the continuous development of artificial intelligence technology and the increasing accessibility of data, various social platforms are committed to using intelligent recommendation algorithms to cater to user preferences. Some platforms even exaggerate facts and push sensational, valueless information to users, leading to the "Screaming Effect" and the "Echo Chamber Effect." The prolonged existence of these effects may result in "Information Cocoon," which is detrimental to the healthy development of individuals and society. To address this issue, different topics can have varying trajectories as online comments ferment, potentially reaching a neutral consensus or resulting in polarization. As a mainstream social platform in China, Weibo users serve as nodes for the dissemination of public opinion information, and the characteristics of information release, reception, forwarding, and commenting all influence the effectiveness of communication. First, we selected the topics "COVID-19" and "IG Electronic Sports Club" as our research subjects, identifying a range of topic popularity through Baidu Index, followed by data collection. Second, we used perplexity and coherence to determine the optimal number of topics for LDA, analyzing the changes in topic feature characteristics over different time periods. Through correlation analysis, we reached the following conclusions: Higher Weibo levels correlate with better information dissemination effectiveness; the quantity of information published on Weibo negatively impacts the forwarding and commenting behaviors; and as events progress, the enthusiasm for information dissemination on Weibo declines, with daily dissemination gradually decreasing.

Identifying influential nodes in social networks from the perspective of attack-defense game.

Influence spread analysis, a critical component of social network studies, focuses on the patterns and effects of information dissemination among interconnected entities. The core of influence spread analysis is to identify influential nodes that involve two distinct aspects: influence maximization (IM) and influence blocking maximization (IBM). However, when IM and IBM occur simultaneously, identifying influential nodes becomes an intricate decision-making challenge. This study addresses identifying influential nodes in social networks through an attack-defense game perspective, where an attacker maximizes influence and a defender minimizes it. We first develop a two-player static zero-sum game model considering resource constraints. Based on the equilibrium strategy of this game, we redefine the concept of influential nodes from various viewpoints. Extensive experiments on synthetic and real-world networks show that, in most cases, the defender preferentially defends critical nodes, while the attacker adopts the decentralized strategy. Only when resources are unevenly matched do both players tend to adopt centralized strategies. This study expands the connotation of influential nodes and provides a novel paradigm for the social network analysis with significant potential applications.

Source localization in signed networks based on dynamic message passing algorithm

Sign characteristics of social networks play a crucial role in the process of information dissemination, and structural balance theory is a significant property of signed networks. Despite some progress in source localization, the sign features of network structures are widely overlooked. Furthermore, the impact of structural balance on tracing remains unclear. In this paper, we explore a model of information propagation in signed networks and investigate source localization in the context of a given network state snapshot. We first propose a modified signed susceptibility-infection-recovery (S-SIR) propagation model, combining the positive/negative transmission rates based on structural balance theory, which is designed to better approximate the real-world social network structure. Next, a signed dynamic message passing algorithm (S-DMP) is proposed for source localization. Simulation experiments show that the S-DMP algorithm can fully utilize both the positive and negative attributes of the edges in the signed network and outperforms other methods in terms of source localization accuracy. These experiments prove that the proposed method can be used for source localization in the early stage of information transmission. Furthermore, we find that the accuracy of source localization is highest when there is a balance between the number of positive and negative edges.

Constraints on AI: Arab Journalists’ experiences and perceptions of governmental restrictions on ChatGPT

ABSTRACT This study investigates the impact of Arab governmental restrictions on journalists’ use of ChatGPT, a leading Generative AI chatbot. Through interviews with 30 journalists from Syria, Libya, Jordan, and Egypt, the research reveals how journalists utilize ChatGPT and face severe censorship. These journalists perceive such censorship as violating their constitutional rights. The findings suggest that current digital legislation enables governments to exert greater control over information dissemination, undermining media independence. The study concludes that government actions to block ChatGPT access constitute a form of indirect or “soft” censorship within existing legal frameworks.

Improving community detection in social networks using enhanced BSO by exploring network structure

Community detection in social networks plays a crucial role in understanding various social phenomena. However, accurately identifying communities is challenging due to the complex nature of social networks. To address this challenge, we propose CD-BSO, an innovative metaheuristic based on brainstorming. CD-BSO leverages network knowledge through specialized initialization and solution generation operators designed to capture social network characteristics. In the initialization phase, a technique combining Depth-First Search (DFS) is employed to consider both connectivity and information diffusion, ensuring accurate community representation. The generation step introduces search operators that consider link formation and node similarity, facilitating convergence towards a community structure that closely resembles the network's actual structure. The evaluation of CD-BSO outperforms recent and well-known community detection methods. The evaluation results were further analyzed using visual analysis, providing valuable insights into the network structure. CD-BSO exhibits significant potential for accurately identifying communities and extracting meaningful information from social networks.

Development, Content Validation and Usability of a Chatbot for Disseminating Scientific Information About Cannabis Among Young Adults

ABSTRACT Despite the high prevalence of cannabis consumption, there is limited information about cannabis based on scientific evidence available and targeted toward young adults, especially considering accessible language and ease of access. The present study aimed to develop the proof of concept of a chatbot for the dissemination of evidence and scientific information about cannabis, focused on a young adult audience. The proof of concept involved stages of literature review, conversation flow development, content evaluation through a panel of experts (n = 10) and usability analysis using a sample of potential users (young adults, n = 10). The chatbot developed presents information on epidemiology, toxicology, acute and chronic effects of cannabis use, information on cannabis use disorder, and the use of cannabinoids for medicinal purposes. The content evaluation through the Health Education Content Validation Instrument concluded that the tool was considered to have a good structure and content (Content Validity Index > 0,78; overall score = 9.0/10.0). The tool also proved to be functional and highly usable according to the analysis with the System Usability Scale (overall usability score = 9.2/10.0). Digital strategies such as chatbots can help in health education and scientific communication within stigmatized areas, such as the consumption of psychoactive substances.

Revealing the hidden wealth in healthcare: an empirical study of intellectual capital disclosure in leading global hospitals

Purpose Under the lens of stakeholder theory, this study aims to explore the dissemination of intellectual capital (IC) information within the healthcare sector. Specifically, the primary objective is to investigate the extent of IC disclosure (ICD) among leading global hospitals and its underlying determinants. Design/methodology/approach This study conducts a manual content analysis of the official websites of 175 leading global hospitals, selected based on The World’s Best Hospitals 2022, and uses a linear regression model to test the research hypotheses. Findings The empirical results underscore an adequate dissemination of IC information by leading global hospitals through their official websites. Moreover, they reveal a positive association between hospital complexity, public status, and online visibility with the extent of IC information disclosed. Originality/value This study represents a pioneering contribution to the examination of determinants influencing online ICD within a global hospital context. It significantly enhances the existing literature on ICD and offers valuable practical implications.

Dissemination of Information on Investor Attention, Firm Size, and Year-End Market Dynamics: An Empirical Study of the Indian Stock Market

This paper investigates the dissemination and presence of the “Year-End Market Surge”, commonly referred to as the “Christmas Rally”, in the Indian stock market. In developed nations, this phenomenon describes a notable increase in stock prices typically observed during the last week of December and the first two trading days of January. Recent reports in the popular press suggest that a similar trend has been witnessed in the Indian stock market over recent years. However, there remains a lack of systematic research on this subject. Therefore, this study rigorously examines whether this market surge, which poses a potential challenge to the Efficient Market Hypothesis (EMH), is observable in the Indian context. Furthermore, the paper explores the dissemination of firm-specific trading patterns to identify characteristics of companies that have consistently delivered positive returns during this period over multiple years. The findings reveal that larger stock portfolios in the Indian market consistently benefit from the Year-End Market Surge effect, delivering higher abnormal returns compared to smaller portfolios. These results provide important insights into the role of firm size in capturing the benefits of this seasonal market anomaly.

Journalistic Ethics in Covering Sharia Economic Issues: An Islamic Perspective

This study explores how the principles of journalistic ethics can be applied in covering issues of Islamic economics. In the era of rapidly developing digital media, journalism plays an important role in disseminating information about Islamic economics, which is increasingly relevant in the global economy. However, the delivery of this information is often faced with ethical challenges, such as objectivity, truth, and public interest, especially in the context of an economic system based on Islamic values. This study analyzes the basic principles of journalistic ethics, such as accuracy, independence, and fairness, and how these values synergize with Islamic teachings, especially in terms of Islamic economics. In addition, this study also discusses Islamic views on the dissemination of information, social justice, and protection of individual rights that can be a guide for journalists in carrying out their duties. Through literature studies and theoretical analysis, this study found that the principles of journalistic ethics are in line with Islamic teachings, especially in terms of upholding justice, transparency, and social responsibility. In conclusion, the integration of journalistic ethics and Islamic perspectives in covering sharia economics can improve the quality of information conveyed to the public, encourage broader awareness of the importance of an economy based on moral and religious values, and minimize the distortion of information that is detrimental to society.

Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

A conceptual framework for integrating digital transformation in healthcare marketing to boost patient engagement and compliance

This paper presents a conceptual framework for integrating digital transformation in healthcare marketing to enhance patient engagement and compliance with treatment regimens. As healthcare systems evolve, the adoption of digital technologies has become imperative for fostering meaningful interactions between patients and providers. The proposed framework emphasizes leveraging digital marketing techniques, data analytics, and personalized communication strategies to strengthen patient-provider relationships and improve health outcomes. At the core of the framework is the use of digital marketing techniques, including social media outreach, content marketing, and mobile applications, to reach patients effectively. These tools facilitate the dissemination of vital health information and resources, enabling patients to make informed decisions about their care. By utilizing data analytics, healthcare organizations can gain insights into patient behaviors, preferences, and demographics, allowing for the customization of marketing efforts to meet the specific needs of diverse patient populations. Personalized communication strategies are pivotal in this framework, as they empower healthcare providers to deliver tailored messages that resonate with individual patients. This personalization fosters a sense of connection and trust, encouraging patients to engage actively in their treatment plans. By utilizing patient data, providers can send reminders, educational content, and motivational messages that enhance treatment adherence and compliance. Moreover, the framework addresses the importance of feedback loops, where healthcare organizations continuously gather and analyze patient responses to marketing efforts. This iterative process allows for the optimization of strategies, ensuring they remain effective and relevant over time. By fostering a culture of engagement, organizations can cultivate long-term relationships with patients, ultimately leading to improved health outcomes. In conclusion, this conceptual framework serves as a strategic guide for healthcare organizations aiming to harness digital transformation in their marketing efforts. By integrating digital marketing techniques, data analytics, and personalized communication strategies, organizations can enhance patient engagement, improve treatment adherence, and positively impact overall health outcomes.

Exploring online health information quality criteria on social media: a mixed method approach

PurposeThis article outlines a research study that ranked health information quality criteria on social media from experts’ perspectives.MethodologyA mixed-method approach (qualitative-quantitative) used in current research. In the qualitative phase a literature review explored existing dimensions for evaluating social media content quality, focusing on identifying common dimensions and attributes. Furthermore, a quantitative method involving experts was utilized to rank the health information quality criteria for social media.ResultsThe findings indicated various dimensions of health information quality in the literature. Out of 17 criteria, accuracy, credibility, and reliability had the highest ranks, while originality, value-added, and amount of data had the lowest ranks, respectively, according to experts.ConclusionThe endeavor to bolster the dissemination of reliable health information on social media demands a sustained commitment to enhancing accountability, transparency, and accuracy, ensuring that users have access to information that is not only informative but also trustworthy.

The Infringement of Deepfake Technology on Personal Privacy and Legal Protection: A Discussion Based on Article 1032 of the Civil Code

The rapid development of deepfake technology has infringed upon personal privacy in the form of the disclosure and misuse of personal information, infringement upon the peace of people's life, and damage to personal image and reputation. The disclosure and misuse of personal information should be analysed from the perspectives of personal use and commercial use of information data; infringement upon the peace of people's life should be examined from the perspectives of fraud and harassment, and malicious pursuit using deepfake technology; and damage to personal image and reputation should be discussed from the two levels of initial damage to personal image and reputation and aggravated damage to personal reputation after dissemination through the media. Article 1032 of the Civil Code clearly defines the scope of privacy protection as the peace of people's private life, private space, private activities and private information, as well as the infringement of privacy rights through means such as spying, intrusion, disclosure and publication, which reflects the legislative inertia to strengthen the protection of privacy and personal information. Article 1032 of the Civil Code, the Personal Information Protection Law, the Provisions on the Administration of Deep Synthesis of Internet-based Information Services, and other laws and regulations together form the current personal privacy protection system. However, existing laws do not provide specific explanations for key terms such as 'leakage', 'intrusion' and 'damage', which leads to uncertainty in the application of the law and makes it difficult to effectively protect personal privacy in actual operations. At the same time, there is a lack of clear definitions of the scenarios and specific standards of behaviour that the technology may involve, which has exacerbated public concerns about personal privacy security. Strengthening legal protection against deepfake technology requires refining legal concepts and drawing on knowledge from comparative law: 'leakage' can be considered the illegal acquisition and dissemination of unauthorised data and information, 'intrusion' can be defined as behaviour that disrupts the normal order of others and causes mental distress, and 'damage' can be understood as behaviour that causes negative evaluation or loss to the image, reputation or property of others. The legislative department should take the lead and cooperate with the Ministry of Industry and Information Technology in formulating specific standards for evaluating behaviour. The deterrent effect of the law should be strengthened by refining relevant legal provisions in the Civil Code and the Personal Information Protection Law and increasing the penalties for violations of the law. This will better balance technological development and personal privacy protection and improve China's personal privacy protection system.