Rethinking inventories in the digital age: the case of the Old Bailey

This article builds on the digitized version of the Old Bailey Proceedings (www.oldbaileyonline.org) by first extracting the indictments from the surrounding text and then subjecting the words they include, and objects they describe, to analysis. This entails working with a corpus of over a million words. At this scale, close reading no longer serves the historian well. It would require far more time than is reasonable or feasible; and a strategy of ‘distant reading’ is adopted here to allow analysis to focus on larger units of text

Masculine Foes, Feminist Woes: A Response to Down Girl

In her book, Down Girl, Manne proposes to uncover the “logic” of misogyny, bringing clarity to a notion that she describes as both “loaded” and simultaneously “politically marginal.” Manne is aware that full insight into the “logic” of misogyny will require not just a “what” but a “why.” Though Manne finds herself largely devoted to the former task, the latter is in the not-too-distant periphery. Manne proposes to understand misogyny, as a general framework, in terms of what it does to women. Misogyny, she writes, is a system that polices and enforces the patriarchal social order (33). That’s the “what.” As for the “why,” Manne suggests that misogyny is what women experience because they fail to live up to the moral standards set out for women by that social order. I find Manne’s analysis insightful, interesting, and well argued. And yet, I find her account incomplete. While I remain fully convinced by her analysis of what misogyny is, I am less persuaded by her analysis of why misogyny is. For a full analysis of the “logic” of misogyny, one needs to understand how the patriarchy manifests in men an interest in participating in its enforcement. Or so I hope to motivate here. I aim to draw a line from the patriarchy to toxic masculinity to misogyny so that we have a clearer picture as to why men are invested in this system. I thus hope to offer here an analysis that is underdeveloped in Manne’s book, but is equally worthy of attention if we want fully to understand the complex machinations underlying misogyny

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Sentiment Analysis and Opinion Mining within Social Networks using Konstanz Information Miner

Evaluations, opinions, and sentiments have become very obvious due to rapid emerging interest in ecommerce which is also a significant source of expression of opinions and analysis of sentiment. In this study, a general introduction on sentiment analysis, steps of sentiment analysis, sentiments analysis applications, sentiment analysis research challenges, techniques used for sentiment analysis, etc., were discussed in detail. With these details given, it is hoped that researchers will engage in opinion mining and sentiment analysis research to attain more successes correlated to these issues. The research is based on data input from web services and social networks, including an application that performs such actions. The main aspects of this study are to statistically test and evaluate the major social network websites: In this case Twitter, because it is has rich data source and easy within social networks tools. In this study, firstly a good understanding of sentiment analysis and opinion mining research based on recent trends in the field is provided. Secondly, various aspects of sentiment analysis are explained. Thirdly, various steps of sentiment analysis are introduced. Fourthly, various sentiment analysis, research challenges are discussed. Finally, various techniques used for sentiment analysis are explained and Konstanz Information Miner (KNIME) that can be used as sentiment analysis tool is introduced. For future work, recent machine learning techniques including big data platforms may be proposed for efficient solutions for opinion mining and sentiment analysi

Public trust in health authorities: Examining Twitter comments on CDC and Fauci during Covid-19

The purpose of the study is to examine public trust in health authorities during COVID-19 and whether individuals\u27 trust in health authorities is influenced by inconsistent health messages. Considering the origin of public trust in the public sphere, the study focuses on the online form of the public sphere- Twitter. As many studies in health communication have implemented large-scale approaches to investigate Twitter data, this study offers a qualitative analysis by conducting a close reading of tweets that mention the Center of Disease Control and Prevention (CDC) and Dr. Anthony Fauci. The results of this research suggest that inconsistency in health guidance and information may potentially hinder public trust in health authorities. Specifically, inconsistency in numbers of COVID-19 metrics may significantly influence individual perceptions of the trustworthiness of health authorities. The rhetorical implications of research findings also suggest that existing partisan divides and general concerns in science may also shape how the public fails to trust during the COVID-19 pandemic

The Impact of Culture and Religion on Digital Forensics: The Study of the Role of Digital Evidence in the Legal Process in Saudi Arabia

This work contributes to the multi-disciplinary community of researchers in computer science, information technology and computer forensics working together with legal enforcement professionals involved in digital forensic investigations. It is focused on the relationship between scientific approaches underpinning digital forensics and the Islamic law underpinning legal enforcement. Saudi Arabia (KSA) is studied as an example of an Islamic country that has adopted international guidelines, such as ACPO, in its legal enforcement procedures. The relationship between Islamic law and scientific ACPO guidelines is examined in detail through the practices of digital forensic practitioners in the process of discovery, preparation and presentation of digital evidence for use in Islamic courts in KSA. In this context, the influence of religion and culture on the role and status of digital evidence throughout the entire legal process has been the main focus of this research. Similar studies in the literature confirm that culture and religion are significant factors in the relationship between law, legal enforcement procedure and digital evidence. Islamic societies, however, have not been extensively studied from this perspective, and this study aims to address issues that arise at both professional and personal levels. Therefore the research questions that this study aims to answer are: in what way and to what extent Islamic religion and Saudi culture affect the status of digital evidence in the KSA legal process and what principles the practitioners have to observe in the way they treat digital evidence in judicial proceedings. The methodology is based on a mixed-method approach where the pilot questionnaire identified legal professionals who come into contact with digital evidence, their educational and professional profiles. Qualitative methods included case studies, interviews and documentary evidence to discover how their beliefs and attitudes influence their trust in digital evidence. The findings show that a KSA judge would trust witnesses more than digital evidence, due to the influence of tradition, which regards justice and law to arise from the relationship between Man and God. Digital evidence, as it arises from the scientific method, is acceptable, but there is underlying lack of trust in its authenticity, reliability and credibility. In the eyes of the legal enforcement professionals working in all areas of the KSA legal process, acceptance of digital evidence in the KSA judicial system can best be improved if knowledge, education and skills of digital forensics specialists is improved also, so that they can be trusted as expert witnesses. This further shows the significance of KSA laws, regulations and education of digital forensic experts as the primary means for establishing trust in digital evidence. Further research following from this study will be focused on comparative studies of other Islamic non-Islamic legal systems as they adopt and adapt western guidelines such as ACPO to their religion, culture and legal systemsSaudi Cultural Bureau,London, U

Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems

We can leverage data and complex systems science to better understand society and human nature on a population scale through language --- utilizing tools that include sentiment analysis, machine learning, and data visualization. Data-driven science and the sociotechnical systems that we use every day are enabling a transformation from hypothesis-driven, reductionist methodology to complex systems sciences. Namely, the emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, with profound implications for our understanding of human behavior. Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture\u27s evolution through its texts using a big data lens. Given the growing assortment of sentiment measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts. Here, we perform detailed, quantitative tests and qualitative assessments of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts. Most importantly they can aid understanding of texts with reliable and meaningful word shift graphs if (1) the dictionary covers a sufficiently large enough portion of a given text\u27s lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories, forming patterns that are meaningful to us. By classifying the emotional arcs for a filtered subset of 4,803 stories from Project Gutenberg\u27s fiction collection, we find a set of six core trajectories which form the building blocks of complex narratives. We strengthen our findings by separately applying optimization, linear decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads. Within stories lie the core values of social behavior, rich with both strategies and proper protocol, which we can begin to study more broadly and systematically as a true reflection of culture. Of profound scientific interest will be the degree to which we can eventually understand the full landscape of human stories, and data driven approaches will play a crucial role. Finally, we utilize web-scale data from Twitter to study the limits of what social data can tell us about public health, mental illness, discourse around the protest movement of #BlackLivesMatter, discourse around climate change, and hidden networks. We conclude with a review of published works in complex systems that separately analyze charitable donations, the happiness of words in 10 languages, 100 years of daily temperature data across the United States, and Australian Rules Football games

Discerning the Language Assessment Literacy of EFL Teachers in Uzbekistan: An Individual, Social, and Sociohistorical Teacher Cognition Inquiry

Language teacher cognitions can be complex, ranging over many different subjects; they can be dynamic, changing over time and under different influences; and they can be systematic, forming unified and cohesive personal and practical theories (Feryok, 2010). One subject matter area that has not been explored in the literature is the relationship between second language (L2) teacher cognition and assessment, also known as (language) assessment literacy – the level of a teacher\u27s engagement with constructing, using, and interpreting a variety of assessment procedures to make decisions about a learner’s language ability (Taylor, 2013). I examine L2 teachers’ cognitions about assessment at the individual level, and then analyze how micro-institutional (social) and macro-sociocultural aspects of their lives as language teachers (including past, present, and future aspects) are shaping teachers’ assessment practices. This investigation focused on a group of 96 in-service university English language teachers in Uzbekistan. The three overarching research questions are: (1) To what extent does the Language Assessment Literacy Survey (Kremmel & Harding, forthcoming), provide valid and actionable information about teachers’ language assessment literacy? (2) How do Uzbekistan EFL teachers talk about their assessment practices and justify the scores they provide for their students? (3) What are the macro-environmental constraints and/or affordances in Uzbekistan that could shape how EFL teachers provide meaningful assessment situations for their students? The data were collected over three months and include teachers’ responses to the Language Assessment Literacy Survey (N = 96), transcripts of five focus group interviews, and transcripts of twelve semi-structured one-on-one interviews. For quantitative analyses, I computed in JASP v.0.8.3.1 the descriptive statistics for the overall survey, conducted an external review of the language assessment literacy literature, and carried out an exploratory factor analysis (EFA). Subsequently, I compared my results with Kremmel and Harding’s to determine the validity of their survey. For qualitative analyses, I used substantive or open coding to discern how/if the participating Uzbekistan EFL teachers are creating relevant and meaningful assessment experiences for their students. The results are discussed in terms of the relationship between the participating Uzbekistan EFL teachers’ cognitions, emotions, and activities of language assessment that they report

Sentiment Analysis for the Low-Resourced Latinised Arabic "Arabizi"

The expansion of digital communication mediums from private mobile messaging into the public through social media presented an opportunity for the data science research and industry to mine the generated big data for artificial information extraction. A popular information extraction task is sentiment analysis, which aims at extracting polarity opinions, positive, negative, or neutral, from the written natural language. This science helped organisations better understand the public’s opinion towards events, news, public figures, and products. However, sentiment analysis has advanced for the English language ahead of Arabic. While sentiment analysis for Arabic is developing in the literature of Natural Language Processing (NLP), a popular variety of Arabic, Arabizi, has been overlooked for sentiment analysis advancements. Arabizi is an informal transcription of the spoken dialectal Arabic in Latin script used for social texting. It is known to be common among the Arab youth, yet it is overlooked in efforts on Arabic sentiment analysis for its linguistic complexities. As to Arabic, Arabizi is rich in inflectional morphology, but also codeswitched with English or French, and distinctively transcribed without adhering to a standard orthography. The rich morphology, inconsistent orthography, and codeswitching challenges are compounded together to have a multiplied effect on the lexical sparsity of the language, where each Arabizi word becomes eligible to be spelled in many ways, that, in addition to the mixing of other languages within the same textual context. The resulting high degree of lexical sparsity defies the very basics of sentiment analysis, classification of positive and negative words. Arabizi is even faced with a severe shortage of data resources that are required to set out any sentiment analysis approach. In this thesis, we tackle this gap by conducting research on sentiment analysis for Arabizi. We addressed the sparsity challenge by harvesting Arabizi data from multi-lingual social media text using deep learning to build Arabizi resources for sentiment analysis. We developed six new morphologically and orthographically rich Arabizi sentiment lexicons and set the baseline for Arabizi sentiment analysis on social media

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields