COVID-19 Tracking Applications: A Human-Centric Analysis

The year 2020 will always be remembered with the imprints left by COVID-19 on our lives. While the pandemic has had many undesirable effects for the whole world, one of its biggest side effects has been the fast digital transformation that has taken place, which was already in progress with the Industry 4.0 era. The readily available technology and wireless communications infrastructures paved the way for a myriad of digital technologies for the containment of the disease using mobile contact tracing applications developed by health authority organizations in many countries. The mounting privacy concerns especially with Bluetooth-enabled proximity tracing and centralized tracking technologies used by these applications have given rise to the development of new privacy-preserving contact tracing protocols. Although these new protocols have alleviated the privacy concerns of citizens to a certain extent, widespread adoption is still far from being the reality. In this paper, we analyze existing contact tracing technologies from a human-centric standpoint by focusing on their privacy implications. We present our comprehensive dataset consisting of the contact tracing application usage information in 94 countries and provide results of a multinational survey we have conducted on the sentiments of people regarding contact tracing applications. The survey results demonstrate that privacy concerns are still the leading deterrent for people when deciding whether to use these applications. Nevertheless, it is a globally accepted argument that the most effective and fastest method for contact tracking will be digital technologies free from human errors and manual procedures. Accordingly, it is concluded that a policy of developing decentralized tracking solutions based entirely on user privacy should be followed, in which independent trusted third parties assume the role of authority in the system architecture, if absolutely necessary, in order to effectively combat the pandemic worldwide. An important feature of the systems to be developed to pave the way for widespread use is to provide the users the right to be forgotten

Evolutionary Multiobjective Feature Selection for Sentiment Analysis

AuthorSentiment analysis is one of the prominent research areas in data mining and knowledge discovery, which has proven to be an effective technique for monitoring public opinion. The big data era with a high volume of data generated by a variety of sources has provided enhanced opportunities for utilizing sentiment analysis in various domains. In order to take best advantage of the high volume of data for accurate sentiment analysis, it is essential to clean the data before the analysis, as irrelevant or redundant data will hinder extracting valuable information. In this paper, we propose a hybrid feature selection algorithm to improve the performance of sentiment analysis tasks. Our proposed sentiment analysis approach builds a binary classification model based on two feature selection techniques: an entropy-based metric and an evolutionary algorithm. We have performed comprehensive experiments in two different domains using a benchmark dataset, Stanford Sentiment Treebank, and a real-world dataset we have created based on World Health Organization (WHO) public speeches regarding COVID-19. The proposed feature selection model is shown to achieve significant performance improvements in both datasets, increasing classification accuracy for all utilized machine learning and text representation technique combinations. Moreover, it achieves over 70% reduction in feature size, which provides efficiency in computation time and space

A RoBERTa Approach for Automated Processing of Sustainability Reports

There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs

Privatization processes as ideological moments: The block sales of large-scale state enterprises in Turkey in the 2000s

In the process of neoliberal transformation in Turkey, what differentiated the 2000s from the previous two decades were the block sale privatizations of large-scale state enterprises such as PETKIM, Turk Telekom, TUPRAS, and ERDEMIR. These block sales, the conditions of which were shaped by political struggles at different levels, were also constitutive political and ideological moments per se, helping to reproduce a particular perception of social reality at the expense of others. This paper will overview and critically problematize the privatization processes of these four enterprises, all completed under the successive AKP governments in power since 2002. By focusing on the apparently technical and economic aspects of the block-sale processes, such as valuation, efficiency enhancement and marketing, the paper calls into question the increased concerns over their transparency, and wonders whether such concerns can be understood as attempts to mask the substantially corrupt nature of capitalist relations of production, which inescapably makes itself felt during these processes

A RoBERTa Approach for Automated Processing of Sustainability Reports

There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs