ABSTRACT
Integrity 2023 is the fourth edition of the successful Workshop on Integrity in Social Networks and Media, held in conjunction with the ACM Conference on Web Search and Data Mining (WSDM) in the past three years. The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms. The event consists of a combination of invited talks by reputed members of the Integrity community from both academia and industry and peer-reviewed contributed talks and posters solicited via an open call-for-papers. © 2023 Owner/Author.
ABSTRACT
From an environmental equity perspective, the aim of this paper is twofold. First, we want to verify to what extent vulnerable population groups resided in areas exposed to high levels of aircraft noise before and during the COVID-19 pandemic (2019 and 2020) in the Montréal census metropolitan area. Second, we want to identify whether the use of an aircraft noise indicator rather than another generates significant variations in the results and consequently in terms of affected areas and populations. With the IMPACT web-application, we model aircraft noise contours from three cumulative (Lden , Ldn , Laeq ,24h) and a single-event (LAmax ) metrics. The model's input data are retrieved by a website for flight tracking. Next, four variables are extracted from the 2016 Statistics Canada census at a fine scale level (dissemination areas): that is, the percentages of low-income individuals, visible minorities, children under 15 years old, and individuals aged 65 and over. The results show a significant drop in population exposed to aircraft noise in 2020 compared to 2019. In addition, the estimates of populations impacted by aircraft noise differ from one indicator to the next. The logistic regression models indicate that the inequities are inconsistent between cumulative and single-event metrics.
ABSTRACT
Web data mining became an easy and important platform for retrieval of useful information. Users prefer World Wide Web more to upload and download data. As increasing growth of data over the internet, it is getting difficult and time consuming for discovering informative knowledge and patterns. Digging knowledgeable and user queried information from unstructured and inconsistent data over the web is not an easy task to perform. Different mining techniques are used to fetch relevant information from web (hyperlinks, contents, web usage logs). Web data mining is a sub discipline of data mining which mainly deals with web. Web data mining is divided into three different types: web structure, web content and web usage mining. Page content mining also called web scrapping is the technique under web mining that is used for extracting the web data. It is used in combination with the deep neural network algorithm to generate a combined data set. This paper mainly focuses on web content mining technique for the generation of covid-19 twitter data set.
ABSTRACT
Large-scale dark web marketplaces have been around for more than a decade. So far, academic research has mainly focused on drug and hacking-related offers. However, data markets remain understudied, especially given their volatile nature and distinct characteristics based on shifting iterations. In this paper, we perform a large-scale study on dark web data markets. We first characterize data markets by using an innovative theoretical legal taxonomy based on the Council of Europe’s Cybercrime Convention and its implementation in Dutch law. The recent Covid-19 pandemic showed that cybercrime has become more prevalent with the increase of digitalization in society. In this context, important questions arise regarding how cybercrime harms are determined, measured, and prioritized. We propose a determination of harm based on criminal law qualifications and sanctions. We also address the empirical question of what the economic activity on data markets looks like nowadays by performing a comprehensive measurement of digital goods based on an original dataset scraped from twelve marketplaces consisting of approximately 28,000 offers from 642 vendors. The resulting analysis combines insights from the theoretical legal framework and the results of the measurement study. To our knowledge, this is the first study to combine these two elements systematically. © 2022, IFIP International Federation for Information Processing.