Extracting Semantic Knowledge from Twitter

Teufl, Peter; Kraxberger, Stefan

doi:10.1007/978-3-642-23333-3_5

Cited by 10 publications

(6 citation statements)

References 9 publications

(11 reference statements)

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“…Discrimination, which is the basis of group-criterion, is a form of intergroup aggression and is defined as a group having a limited and identifiable set of traits that sets them apart from others (Thomsen, 2013). Discrimination occurs when an individual is treated unfairly compared to others in a similar situation because of their skin color, religion, disability, age, or sex (Teufl and Kraxberger, 2011). Discrimination in the form of “prejudice-related discrepancies,” can result from an individual automatically categorizing groups of people, also known as stereotyping (Frijda, 1996).…”

Section: Resultsmentioning

confidence: 99%

Characterizing and Measuring Maliciousness for Cybersecurity Risk Assessment

et al. 2018

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Cyber attacks have been increasingly detrimental to networks, systems, and users, and are increasing in number and severity globally. To better predict system vulnerabilities, cybersecurity researchers are developing new and more holistic approaches to characterizing cybersecurity system risk. The process must include characterizing the human factors that contribute to cyber security vulnerabilities and risk. Rationality, expertise, and maliciousness are key human characteristics influencing cyber risk within this context, yet maliciousness is poorly characterized in the literature. There is a clear absence of literature pertaining to human factor maliciousness as it relates to cybersecurity and only limited literature relating to aspects of maliciousness in other disciplinary literatures, such as psychology, sociology, and law. In an attempt to characterize human factors as a contribution to cybersecurity risk, the Cybersecurity Collaborative Research Alliance (CSec-CRA) has developed a Human Factors risk framework. This framework identifies the characteristics of an attacker, user, or defender, all of whom may be adding to or mitigating against cyber risk. The maliciousness literature and the proposed maliciousness assessment metrics are discussed within the context of the Human Factors Framework and Ontology. Maliciousness is defined as the intent to harm. Most maliciousness cyber research to date has focused on detecting malicious software but fails to analyze an individual’s intent to do harm to others by deploying malware or performing malicious attacks. Recent efforts to identify malicious human behavior as it relates to cybersecurity, include analyzing motives driving insider threats as well as user profiling analyses. However, cyber-related maliciousness is neither well-studied nor is it well understood because individuals are not forced to expose their true selves to others while performing malicious attacks. Given the difficulty of interviewing malicious-behaving individuals and the potential untrustworthy nature of their responses, we aim to explore the maliciousness as a human factor through the observable behaviors and attributes of an individual from their actions and interactions with society and networks, but to do so we will need to develop a set of analyzable metrics. The purpose of this paper is twofold: (1) to review human maliciousness-related literature in diverse disciplines (sociology, economics, law, psychology, philosophy, informatics, terrorism, and cybersecurity); and (2) to identify an initial set of proposed assessment metrics and instruments that might be culled from in a future effort to characterize human maliciousness within the cyber realm. The future goal is to integrate these assessment metrics into holistic cybersecurity risk analyses to determine the risk an individual poses to themselves as well as other networks, systems, and/or users.

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Section: Resultsmentioning

confidence: 99%

Characterizing and Measuring Maliciousness for Cybersecurity Risk Assessment

et al. 2018

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“…Moreover, proposals for eParticipation platforms addressing both governments and citizens are put forth [51,60,74,78]. Recommendations for government adoption of social media platforms based on citizen message analyses are also made [68,83]. Two studies delve into the scalability of online consultations and the democratic considerations in their design [41,79].…”

Section: Eparticipation Effectsmentioning

confidence: 99%

The Use of Artificial Intelligence in eParticipation: Mapping Current Research

Vasilakopoulos,

Tavantzis,

Promikyridis

et al. 2024

Future Internet

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Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.

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“…First step is preparation of data set and save it on the system at a point called the information bank [18]. The second step is clean some symbols and text that has no value in the field of emotion analysis, such as HTML decoding, @mention, URL links, hashtag / numbers, space, UTF-8.…”

Section: Processing Datamentioning

confidence: 99%

Data Mining and Revealing Hidden Sentiment in Tweets Using Spark

Aqlan¹

2022

IJDST

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Data science is important and scientific value in our lives because it is multi-fields, it's the science that uses scientific methods, processes, algorithms, and systems for the purpose of extracting knowledge and ideas from data whether this data is organized or not. Data science is called 21st century oil to highlight its importance and scientific value in our lives. We paid great attention in this research paper, where we achieved three main steps in the field of data analysis, collecting data from different sources in the Internet and then storing the data within the system, the second step cleaning the data in order to obtain structured data and then applying the algorithms that are responsible for classifying the data. In pursuit of development, we have collected more than 1600000 tweets about the educational process and the possibility of future online education. We give great attention to this field of Sentiment Analysis (SA) and the use of modern Spark technology, which has achieved great success since its emergence. We have succeeded in using Spark in getting a good result, handling data quickly and accurately, which encouraged us to test it on two algorithms of Machine Learning, Support Vector Machine (SVM) & Maximum Entropy (Max Ent).

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Extracting Semantic Knowledge from Twitter

Cited by 10 publications

References 9 publications

Characterizing and Measuring Maliciousness for Cybersecurity Risk Assessment

Characterizing and Measuring Maliciousness for Cybersecurity Risk Assessment

The Use of Artificial Intelligence in eParticipation: Mapping Current Research

Data Mining and Revealing Hidden Sentiment in Tweets Using Spark

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