Should You Believe Wikipedia?

As we interact online we are creating new kinds of knowledge and community. How are these communities formed? How do we know whether to trust them as sources of information? In other words, Should we believe Wikipedia? This book explores what community is, what knowledge is, how the internet facilitates new kinds of community, and how knowledge is shaped through online collaboration and conversation. Along the way the author tackles issues such as how we represent ourselves online and how this shapes how we interact, why there is so much bad behavior online and what we can do about it. And the most important question of all: What can we as internet users and designers do to help the internet to bring out the best in us all?

Online Trolling: The Impact of Antisocial Online Content, Social Media Use, and Gender

The present study aimed to observe the relationships between online trolling, exposure to antisocial online content, frequency of social media use, and gender, using the GAM as a theoretical framework. Four hundred twenty-nine Brazilian internet users (mean = 25.07 years; SD = 7.59; EP = 0.36), most of whom were women (71.8%), participated in the survey. Bivariate correlations indicated a positive relationship between online trolling, exposure to antisocial online content (r = 0.12; p < 0.01), Facebook use (r = 0.21; p < 0.01), Twitter Use (r = 0.12; p < 0.01), and gender (r = 0.15; p < 0.01). An explanatory model including these variables was tested, and obtained a significant model fit (GFI = 0.99; Comparative Fit-Index = 0.99; Tucker Lewis Index = 0.97; Root Mean Square Residual = 0.02; RMSEA = 0.02 | CI = 0 .01–0.07 |). Were also observed indirect effects for exposure to antisocial online content through Twitter use and Facebook use on trolling (λ = 0.03; CI = 0.01–0.05; p < 0.05). It is possible to conclude that the research objectives were fulfilled, emphasizing the role of situational variables in the understanding of online trolling.

Discrediting Strategy in Internet Comments to Publications made by the Mayors of New York and London

This research featured the lingua-personolological aspect of linguistic means and speech techniques connected with the use of discrediting strategy in online comments aimed at discrediting the authorities. The study was based on the methods of continuous sampling and linguistic analysis of official Facebook and Twitter pages of the mayors of New York and London. Lowering proved to be the leading strategy used in comments aimed at discrediting the government. The research revealed the prevailing tactics and techniques of the speech strategy for lowering, e.g. "analysis minus", insult, accusation, etc. Internet users appeared to have an individual style and vocabulary choice that depended on various subjective factors. The comments showed little trust for the government, which makes discrediting the authorities an important contemporary issue.

A Study on Hot Topics in Social Media of Tokyo 2020 Olympic Games

During the Tokyo 2020 Olympic Games, Sina Weibo, as a high-frequency platform, has a wide range of topics and a large number of participants, and is the main channel for Internet users to obtain information. "People's Daily" is the media with high influence on social hot events in the wave microblog, from the opening day of the 2020 Tokyo Olympic Games to the end of the closing ceremony, among all the Olympic Games-related topics released by People's Daily, there were 343 topics with more than 50,000 likes, and 343 topics were used as hot topics for research. Among the 343 hot topics, 64 were table tennis-related topics, and table tennis was the sport with the highest attention among the hot topics. The social network method was used to quantify the People's Daily hot topics, establish a 2-mode network, study social actors as well as social structure from the perspective of relationships, and explain the structure of the 2-mode network and the influence of the macro-level structure on actors.

An Explainable AI-based Intrusion Detection System for DNS over HTTPS (DoH) Attacks

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91\%), recall (99.92\%) and F1 score (99.91\%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model.

An Explainable AI-based Intrusion Detection System for DNS over HTTPS (DoH) Attacks

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91\%), recall (99.92\%) and F1 score (99.91\%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model.

Internet Use, Users, and Cognition: Determination of the Cognitive Relationships Between Internet-Based Technology and Internet Users

This study aims to investigate growing Internet use in relation to cognition. Existing literature suggests human capability to utilize the Internet as an external (transactive) memory source. Formational mechanisms of such transactive memory systems and comparative effects of Internet use on transactive memory and semantic memory are both relatively unknown points of research explored in this study.This study comprises two experimental memory task surveys, confirming and yielding findings in memory research. Semantic memory is negatively affected by notions of information saved online. An adaptive dynamic is also revealed—1) as users often have a vague idea of desired information before searching for it on the Internet, first accessing semantic memory serves as an aid for subsequent transactive memory use and 2) successful initial transactive memory access eliminates the need for subsequently accessing semantic memory for desired information. Internet users form and reinforce transactive memory systems with the Internet by repeatedly defaulting to first accessing semantic memory then transactive memory or to accessing transactive memory only, and decrease reliance on transactive memory systems by repeatedly defaulting to only semantic memory. Users have some degree of control over transactive memory systems they engage in, a phenomenon to be potentially explored in future research directions.

Health-related fake news during the COVID-19 pandemic: perceived trust and information search

PurposeHealth-related online fake news (HOFN) has become a major social problem. HOFN can lead to the spread of ineffective and even harmful remedies. The study aims to understand Internet users' responses to HOFN during the coronavirus (COVID-19) pandemic using the protective action decision model (PADM).Design/methodology/approachThe authors collected pandemic severity data (regional number of confirmed cases) from government websites of the USA and China (Studies 1 and 2), search behavior from Google and Baidu search engines (Studies 1 and 2) and data regarding trust in two online fake news stories from two national surveys (Studies 2 and 3). All data were analyzed using a multi-level linear model.FindingsThe research detected negative time-lagged relationships between pandemic severity and regional HOFN search behavior by three actual fake news stories from the USA and China (Study 1). Importantly, trust in HOFN served as a mediator in the time-lagged relationship between pandemic severity and search behavior (Study 2). Additionally, the relationship between pandemic severity and trust in HOFN varied according to individuals' perceived control (Study 3).Originality/valueThe authors' results underscore the important role of PADM in understanding Internet users' trust in and search for HOFN. When people trust HOFN, they may seek more information to implement further protective actions. Importantly, it appears that trust in HOFN varies with environmental cues (regional pandemic severity) and with individuals' perceived control, providing insight into developing coping strategies during a pandemic.

Comparative study between the effect of television advertising, social media of instagram, and brand image in the decision of purchasing new product

Television advertising is one of the most effective methods used by companies to introduce and give information about a product to their target consumers. However, as technology advances in the early 2000s, the number of internet users in the world and Indonesia has increased annually. There are currently 160 million Indonesian people who are actively using social media, whose spending 3 hours 26 minutes using social media and 3 hours 4 minutes watching television. The current study was carried out in investigating the effect of television advertising, social media, and brand image on consumers' decision in purchasing new products. In this case, the researcher collected data from 250 respondents who used aromatherapy wind oil throughout Indonesia. The results of this study are expected to be used to improve the manufacture of advertising media plans that will be used by companies in the current digitalization era. The results of this study indicated that the role of television advertising greatly influences purchasing decisions compared to Instagram social media for a new product.