以Web of Science数据库核心合集收录的1993篇文献为研究对象,采用CiteSpace可视化软件梳理Disinformation的研究动态和演进过程,使用5W2H分析法探讨Disinformation的潜在利益、传播动机等具体内涵。结合文献分析总结演化规律并对所预设...以Web of Science数据库核心合集收录的1993篇文献为研究对象,采用CiteSpace可视化软件梳理Disinformation的研究动态和演进过程,使用5W2H分析法探讨Disinformation的潜在利益、传播动机等具体内涵。结合文献分析总结演化规律并对所预设的7个问题进行系统性回答,就我国Disinformation的概念认知和信息迷雾治理提出建议。展开更多
“civil discourse”amongst multiple individuals with diverse viewpoints is necessary to move toward truth,to maintain democratic buoyancy,and to get the most accurate read on how best to move forward toward our collec...“civil discourse”amongst multiple individuals with diverse viewpoints is necessary to move toward truth,to maintain democratic buoyancy,and to get the most accurate read on how best to move forward toward our collective good,civil discourse is nonetheless under catastrophic threat by contemporary forces that include the sloppy use of the term“hate speech”;the“libelling by labeling”(aka“cancelling”)in the public square of social media;technologically powered disinformation campaigns;and the growth of“safetyism”in academia.In light of these threats,the goal must be to convince educators,particularly philosophical educators,of the need to adopt a whole new focus in education,namely one that puts a spotlight on the fact that the utilization of the freedom of speech to destroy the freedom of speech of others utterly undermines the positive value of freedom of speech.In order to motivate individuals to turn their back on the dopamine rush of shutting someone down,educators must also spend a great deal of time showcasing the merits of“civil discourse”by providing young people with extensive experience in engaging in facilitated“civil discourse”(aka Communities of Philosophical Inquiry)so that its value can be woven into a personal commitment.展开更多
The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Indi...The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.展开更多
This paper looks at the professional and ethical failure of news organizations in reporting wars, conflicts, and crises Principles of press freedom, objectivity, and impartiality are not respected, and the journalism ...This paper looks at the professional and ethical failure of news organizations in reporting wars, conflicts, and crises Principles of press freedom, objectivity, and impartiality are not respected, and the journalism practice is different in times of war from in normal times. Very often, the news organization sides with the position of its country, and the journalist becomes ＂nationalistic＂ and ＂patriotic＂ and sides with his/her country＇s position towards the war at the expense of impartiality, objectivity, fairness, and ethics of journalism practices. Very often, the news media are used by the military and the political forces to frame wars and conflicts along the lines of the ＂national security＂ and ＂national interests＂. The second Gulf War, 9/11 events in 2001, the Afghan War, and the third Gulf War are used as case studies to assess the different kinds of mechanisms, techniques,＇and ＂rituals＂ practiced by the media to cover and report wars and conflicts to their audiences and world opinion. Results of the study suggest that an alternative theory--＂government press coalition theory＂——should be developed to explain journalism practices and behaviors during wars and conflicts.展开更多
Cyberterrorism poses a significant threat to the national security of the United States of America (USA), with critical infrastructure, such as commercial facilities, dams, emergency services, food and agriculture, he...Cyberterrorism poses a significant threat to the national security of the United States of America (USA), with critical infrastructure, such as commercial facilities, dams, emergency services, food and agriculture, healthcare and public health, and transportation systems virtually at risk. Consequently, this is due primarily to the country’s heavy dependence on computer networks. With both domestic and international terrorists increasingly targeting any vulnerabilities in computer systems and networks, information sharing among security agencies has become critical. Cyberterrorism can be regarded as the purest form of information warfare. This literature review examines cyberterrorism and strategic communications, focusing on domestic cyberterrorism. Notable themes include the meaning of cyberterrorism, how cyberterrorism differs from cybercrime, and the threat posed by cyberterrorism to the USA. Prevention and deterrence of cyberterrorism through information sharing and legislation are also key themes. Finally, gaps in knowledge are identified, and questions warranting additional research are outlined.展开更多
Artificial Intelligence (AI) technologies have intentionally and unintentionally been used to spread false information on all different types of subjects. Throughout the COVID-19 pandemic, there was a pool of differen...Artificial Intelligence (AI) technologies have intentionally and unintentionally been used to spread false information on all different types of subjects. Throughout the COVID-19 pandemic, there was a pool of different information that was being presented to the public, a lot of it contradicting one another. False information spreads regardless of whether there is intent to mislead or misinform whereas AI is not able to decipher what type of information it is pushing to the public is correct and what is not. This mass spread of information through online platforms has been coined as an Infodemic where it is considered a massive volume of information, both online and offline. It includes deliberate attempts to disseminate false information to undermine the public health response and advance alternative agendas of groups or individuals. An infodemic can be incredibly dangerous to society greatly affecting the ability of communities, societies, and countries to control and stop the pandemic due to the abundance of different information in combating the health crisis. This article assesses and evaluates the role of Artificial Intelligence (AI) technologies in helping to spread disinformation during the COVID-19 pandemic. It reviews and evaluates the information curation in modern media, the relationship between AI and disinformation, and the challenges of disinformation campaigns. It further outlines the impact of social media platforms on infodemic and their influence in spreading disinformation during the COVID-19 pandemic. This article analyzes several data mining studies that used different machine learning techniques to identify the influence of disinformation tactics on the COVID-19 pandemic associated with the Twitter platform. It further continues exploring the investigation of the number of influential tweets, the type of users, the levels of credibility of URLs, and the type and effect of social media bots. Finally, the authors assess and conclude how disinformation is widely prevalent throughout social media during the COVID-19 pandemic as well as illustrate the surveys that categorize the prevalence of users involved in the conversation about disinformation separated by country including the percentage of users posting tweets and retweeting news URLs, and the future work in combating the rapid disinformation campaigns and their ethical implication impact.展开更多
Disinformation,often known as fake news,is a major issue that has received a lot of attention lately.Many researchers have proposed effective means of detecting and addressing it.Current machine and deep learning base...Disinformation,often known as fake news,is a major issue that has received a lot of attention lately.Many researchers have proposed effective means of detecting and addressing it.Current machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual information.We introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and classification.In this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper format.Then,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic features.The obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of misinformation.Subsequently,the discovered frequent patterns are used as features for fake news classification.This framework is evaluated with eight classifiers,and their performance is assessed with various metrics.Extensive experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.展开更多
文摘以Web of Science数据库核心合集收录的1993篇文献为研究对象,采用CiteSpace可视化软件梳理Disinformation的研究动态和演进过程,使用5W2H分析法探讨Disinformation的潜在利益、传播动机等具体内涵。结合文献分析总结演化规律并对所预设的7个问题进行系统性回答,就我国Disinformation的概念认知和信息迷雾治理提出建议。
文摘“civil discourse”amongst multiple individuals with diverse viewpoints is necessary to move toward truth,to maintain democratic buoyancy,and to get the most accurate read on how best to move forward toward our collective good,civil discourse is nonetheless under catastrophic threat by contemporary forces that include the sloppy use of the term“hate speech”;the“libelling by labeling”(aka“cancelling”)in the public square of social media;technologically powered disinformation campaigns;and the growth of“safetyism”in academia.In light of these threats,the goal must be to convince educators,particularly philosophical educators,of the need to adopt a whole new focus in education,namely one that puts a spotlight on the fact that the utilization of the freedom of speech to destroy the freedom of speech of others utterly undermines the positive value of freedom of speech.In order to motivate individuals to turn their back on the dopamine rush of shutting someone down,educators must also spend a great deal of time showcasing the merits of“civil discourse”by providing young people with extensive experience in engaging in facilitated“civil discourse”(aka Communities of Philosophical Inquiry)so that its value can be woven into a personal commitment.
基金Abu Dhabi University’s Office of sponsored programs in the United Arab Emirates(Grant Number:19300752)funded this endeavor.
文摘The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.
文摘This paper looks at the professional and ethical failure of news organizations in reporting wars, conflicts, and crises Principles of press freedom, objectivity, and impartiality are not respected, and the journalism practice is different in times of war from in normal times. Very often, the news organization sides with the position of its country, and the journalist becomes ＂nationalistic＂ and ＂patriotic＂ and sides with his/her country＇s position towards the war at the expense of impartiality, objectivity, fairness, and ethics of journalism practices. Very often, the news media are used by the military and the political forces to frame wars and conflicts along the lines of the ＂national security＂ and ＂national interests＂. The second Gulf War, 9/11 events in 2001, the Afghan War, and the third Gulf War are used as case studies to assess the different kinds of mechanisms, techniques,＇and ＂rituals＂ practiced by the media to cover and report wars and conflicts to their audiences and world opinion. Results of the study suggest that an alternative theory--＂government press coalition theory＂——should be developed to explain journalism practices and behaviors during wars and conflicts.
文摘Cyberterrorism poses a significant threat to the national security of the United States of America (USA), with critical infrastructure, such as commercial facilities, dams, emergency services, food and agriculture, healthcare and public health, and transportation systems virtually at risk. Consequently, this is due primarily to the country’s heavy dependence on computer networks. With both domestic and international terrorists increasingly targeting any vulnerabilities in computer systems and networks, information sharing among security agencies has become critical. Cyberterrorism can be regarded as the purest form of information warfare. This literature review examines cyberterrorism and strategic communications, focusing on domestic cyberterrorism. Notable themes include the meaning of cyberterrorism, how cyberterrorism differs from cybercrime, and the threat posed by cyberterrorism to the USA. Prevention and deterrence of cyberterrorism through information sharing and legislation are also key themes. Finally, gaps in knowledge are identified, and questions warranting additional research are outlined.
文摘Artificial Intelligence (AI) technologies have intentionally and unintentionally been used to spread false information on all different types of subjects. Throughout the COVID-19 pandemic, there was a pool of different information that was being presented to the public, a lot of it contradicting one another. False information spreads regardless of whether there is intent to mislead or misinform whereas AI is not able to decipher what type of information it is pushing to the public is correct and what is not. This mass spread of information through online platforms has been coined as an Infodemic where it is considered a massive volume of information, both online and offline. It includes deliberate attempts to disseminate false information to undermine the public health response and advance alternative agendas of groups or individuals. An infodemic can be incredibly dangerous to society greatly affecting the ability of communities, societies, and countries to control and stop the pandemic due to the abundance of different information in combating the health crisis. This article assesses and evaluates the role of Artificial Intelligence (AI) technologies in helping to spread disinformation during the COVID-19 pandemic. It reviews and evaluates the information curation in modern media, the relationship between AI and disinformation, and the challenges of disinformation campaigns. It further outlines the impact of social media platforms on infodemic and their influence in spreading disinformation during the COVID-19 pandemic. This article analyzes several data mining studies that used different machine learning techniques to identify the influence of disinformation tactics on the COVID-19 pandemic associated with the Twitter platform. It further continues exploring the investigation of the number of influential tweets, the type of users, the levels of credibility of URLs, and the type and effect of social media bots. Finally, the authors assess and conclude how disinformation is widely prevalent throughout social media during the COVID-19 pandemic as well as illustrate the surveys that categorize the prevalence of users involved in the conversation about disinformation separated by country including the percentage of users posting tweets and retweeting news URLs, and the future work in combating the rapid disinformation campaigns and their ethical implication impact.
文摘Disinformation,often known as fake news,is a major issue that has received a lot of attention lately.Many researchers have proposed effective means of detecting and addressing it.Current machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual information.We introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and classification.In this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper format.Then,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic features.The obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of misinformation.Subsequently,the discovered frequent patterns are used as features for fake news classification.This framework is evaluated with eight classifiers,and their performance is assessed with various metrics.Extensive experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.
