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Purpose In March 2019, Facebook updated its security procedures requesting ID verification for people who wish to advertise or promote political posts of adverts. The announcement received little media coverage even though it is an interesting development in the battle against fake news. This paper aims to review the current literature on different approaches in the battle against the spread of fake news, including the use of computer algorithms, artificial intelligence (AI) and introduction of ID checks. Design/methodology/approach Critical to the evaluation is consideration into ID checks as a means to combat the spread of fake news. To understand the process and how it works, the team undertook a social experiment combined with reflective analysis to better understand the impact of ID check policies when combined with other standards policies of a typical platform. Findings The analysis identifies grave concerns. In a wider context, standardising such policy will leave political activists in countries vulnerable to reprisal from authoritarian regimes. Other victims of the impacts include people who use fake names to protect the identity of adopted children or to protect anonymity from abusive partners. Originality/value The analysis also points to the fact that troll armies could bypass these checks rendering the use of ID checks less effective in the battle to combat fake news.

The paper focus on statistical analysis of separate, combined and partial hybridization performance of evolutionary algorithm and neighborhood searcher with a goal to find an intelligent way of hybridization of evolutionary algorithms. On the Traveling Salesman Problem (TSP) we measured the influence of hybridization a 2-opt heuristic-based local searcher into the evolutionary algorithm. Evolutionary Algorithm gives a diversification, while 2-opt improves intensification. The TSP is nowadays already solved for small instances rather efficiently by exact algorithms (e.g concorde) and by local search heuristics as LKH by Helsgaun. Nevertheless, the paper shows statistical analysis that intelligently hybridized algorithm combines good qualities from the both applied components and outperforms each individual method and suggest what level and type of hybridization is best for a given problem to make them intelligent. In tests we applied hybridization at various percentages (level) of evolutionary algorithm iterations. The main contribution of the paper is to show statistical analysis of hybridized evolutionary algorithms. For that purpose we used well known statistical tools. Since the evaluation scores were not normally distributed, the nonparametric Kruskal-Wallis Test (KWT) was used instead of the standard one-way ANOVA. The differences were considered to be statistically significant in cases where the estimated p-values of statistical tests were less than or equal to 0.05. The analysis with KWT showed that there exist statistically significant differences in place of hybridization. The analysis revealed significant differences in all levels of the hybridization. However, intensifying the level of hybridization further increased the p-value of the KWT, which means that the place of hybridization becomes of a less importance when the level of hybridization increases.

Academic research shows increase reliance of online users on social media as a main source of news and information. Researchers found that young users are particularly inclined to believe what they read on social media without adequate verification of the information. There has been some research to study the spread of misinformation and identification of key variables in developing simulations of the process. Current literature on combating misinformation focuses on individuals and neglects social newsgroups-key players in the dissemination of information online. Using benchmark variables and values from the literature, the authors simulated the process using Biolayout; a big data-modeling tool. The results show social newsgroups have significant impact in the explosion of misinformation as well as combating misinformation. The outcome has helped better understand and visualize how misinformation travels in the spatial space of social media.

One of the key features of social networks is that users are able to share information, and through cascades of sharing information, this information may reach a large number of individuals. The high availability of user-provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. With lack of means to verify information, social media has been accused of becoming a hot bed for sharing of misinformation. Facebook, as one of the largest social networking services, has been facing widespread criticism on how its newsfeed algorithm is designed thus amplifying dissemination of misinformation. In late 2016, Facebook revealed plans to address fake news on Facebook newsfeeds. In this work, we study the methods Facebook has proposed to combat the spread of misinformation and compare it with our previously proposed approach called ‘Right-click Authenticate’. By analyzing the Business Process Modeling and Notation of both approaches, this paper suggests some key weaknesses and improvements social media companies need to consider when tackling the spread of misinformation online.

The spread of misinformation online is specifically amplified by use of social media, yet the tools for allowing online users to authenticate text and images are available though not easily accessible. The authors challenge this view suggesting that corporations’ responsible for the development of browsers and social media websites need to incorporate such tools to combat the spread of misinformation. As a step stone towards developing a formula for simulating spread of misinformation, the authors ran theoretical simulations which demonstrate the unchallenged spread of misinformation which users are left to authenticate on their own, as opposed to providing the users means to authenticate such material. The team simulates five scenarios that gradually get complicated as variables are identified and added to the model. The results demonstrate a simulation of the process as proof-of-concept as well as identification of the key variables that influence the spread and combat of misinformation online.