There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.

No / Artificial intelligence (AI) technology is becoming increasingly prevalent in society, offering a range of benefits and opportunities. However, with the rise of AI comes new challenges, particularly in hate speech. Hate speech, a type of expression that incites hatred or violence against individuals or groups based on ethnicity, religion, or other characteristics, has become a growing concern in Malaysia, with social media and online platforms becoming a breeding ground for such speech. In this context, AI technology has emerged as a potential solution for monitoring and regulating hate speech, but it also presents legal and ethical challenges that must be addressed. In view of double edge sword roles played by the development of AI, this article analyses the legal recourse available in Malaysia for disruptive AI technology and hate speech. The authors claim that AI systems are prone to errors and biases and that there is a risk of relying too much on such plans at the expense of human judgement. There are also concerns regarding the impact of AI on free expression and privacy rights. In addition, the author suggests that artificial intelligence be appropriately regulated to ensure that it is consistent with international human rights standards and national laws. / This research was supported by the Ministry of Education (MOE) through the Fundamental Research Grant Scheme (FRGS/1/2020/SSI0/MSU/03/1).

In this thesis, we have studied the problem of detecting automated Twitter accounts related to the Ukraine conflict using supervised learning. A striking problem with the collected data set is that it was initially lacking a ground truth. Traditionally, supervised learning approaches rely on manual annotation of training sets, but it incurs tedious work and becomes expensive for large and constantly changing collections. We present a novel approach to synthetically generate large amounts of labeled Twitter accounts for detection of automation using a rule-based classifier. It significantly reduces the effort and resources needed and speeds up the process of adapting classifiers to changes in the Twitter-domain. The classifiers were evaluated on a manually annotated test set of 1,000 Twitter accounts. The results show that rule-based classifier by itself achieves a precision of 94.6% and a recall of 52.9%. Furthermore, the results showed that classifiers based on supervised learning could learn from the synthetically generated labels. At best, the these machine learning based classifiers achieved a slightly lower precision of 94.1% compared to the rule-based classifier, but at a significantly better recall of 93.9% / Detta exjobb har undersökt problemet att detektera automatiserade Twitter-konton relaterade till Ukraina-konflikten genom att använda övervakade maskininlärningsmetoder. Ett slående problem med den insamlade datamängden var avsaknaden av träningsexempel. I övervakad maskininlärning brukar man traditionellt manuellt märka upp en träningsmängd. Detta medför dock långtråkigt arbete samt att det blir dyrt förstora och ständigt föränderliga datamängder. Vi presenterar en ny metod för att syntetiskt generera uppmärkt Twitter-data (klassifieringsetiketter) för detektering av automatiserade konton med en regel-baseradeklassificerare. Metoden medför en signifikant minskning av resurser och anstränging samt snabbar upp processen att anpassa klassificerare till förändringar i Twitter-domänen. En utvärdering av klassificerare utfördes på en manuellt uppmärkt testmängd bestående av 1,000 Twitter-konton. Resultaten visar att den regelbaserade klassificeraren på egen hand uppnår en precision på 94.6% och en recall på 52.9%. Vidare påvisar resultaten att klassificerare baserat på övervakad maskininlärning kunde lära sig från syntetiskt uppmärkt data. I bästa fall uppnår dessa maskininlärningsbaserade klassificerare en något lägre precision på 94.1%, jämfört med den regelbaserade klassificeraren, men med en betydligt bättre recall på 93.9%.

No / Security is a function of the trust that is associated with the active variables in a system. Thus, the human factor being the most critical element in security systems, the security perimeter could be defined in relation to the human trust level. Trust level could be measured via positive identification of the person/device on the other side of the interaction medium, using various authentication schemes; location-based being one of the latest. As for the location-based services, the identity of a customer remains hazy as long as his location is unknown; he virtually remains a ghost in the air, with implications on trust. This paper reviews the various location-based authentication techniques with a focus on the role that GPS could play in optimising this authentication approach. It advocates the urgent need to make all transmission devices GPS-compliant as a way forward, despite the privacy issues that might arise.