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With the recent proliferation of social media, the spread of disinformation and misinformation has become a global risk. This study analyzes and characterizes the relationship between human emotions and other elements (social bots and echo chambers), which are major factors in the spread of information, including disinformation and misinformation. Specifically, we obtain from X (formerly Twitter) the case that was fact-checked as disinformation in Japan and analyze the relationship between each factor in a time sequence using existing tools and methods. After confirming the similar results to previous studies that emotional posts were more likely to be spread, our analysis showed the possibility that social bots were highly influential in amplifying negative sentiment in the early stages of spread. Regarding emotions and echo chambers, we observed that the increase in negative emotions and the formation of echo chambers followed a similar time sequence. In addition, we confirmed that the amplification of negative emotions precedes the formation of echo chambers. The results of this analysis suggested the importance of detecting social bot activity in the early stages of information diffusion and controlling the amplification of negative emotions.

The proliferation of disinformation has become an issue in recent years due to the widespread use of social media. This study analyzes the recent activities of social bots in Japan, which may be used for the purpose of spreading disinformation, and clarifies the characteristics of influential social bots. Specifically, we collected data from X (formerly Twitter) on several news items that had a great impact in Japan, and analyzed the spread of information by social bots using a combination of existing tools and methods. Our analysis compared the number and percentage of social bots on X in Japanese cases to existing research analyzing those during the 2016 US presidential election, and clarified what kind of social bots are influencing the information diffusion. In all cases we examined, our analysis showed that social bot activity in Japan was more active than during the 2016 US presidential election. We also found that humans are spreading posts created by social bots, as was the case during the 2016 US presidential election. Furthermore, we confirmed that the characteristics of social bots reposted by humans on X in Japan are similar to human accounts, and it is difficult to detect them using only the profile information on the X account page.

Vulnerability and attack information must be collected to assess the severity of vulnerabilities and prioritize countermeasures against cyberattacks quickly and accurately. Common Vulnerabilities and Exposures is a dictionary that lists vulnerabilities and incidents, while Common Attack Pattern Enumeration and Classification is a dictionary of attack patterns. Direct identification of common attack pattern enumeration and classification from common vulnerabilities and exposures is difficult, as they are not always directly linked. Here, an approach to directly find common links between these dictionaries is proposed. Then, several patterns, which are combinations of similarity measures and popular algorithms such as term frequency–inverse document frequency, universal sentence encoder, and sentence BERT, are evaluated experimentally using the proposed approach. Specifically, two metrics, recall and mean reciprocal rank, are used to assess the traceability of the common attack pattern enumeration and classification identifiers associated with 61 identifiers for common vulnerabilities and exposures. The experiment confirms that the term frequency–inverse document frequency algorithm provides the best overall performance.