Wikipedia Content Research Articles

Transforming Wikipedia text into audio through machine learning

Purpose The purpose of this study is to navigate the process of transforming Wikipedia articles into audio files for library readers. The system provides a feasible manner of listening to Wikipedia content, accommodating diverse learning preferences and enlarging knowledge in education society. Design/methodology/approach This framework has been constructed using the Python programming languages in the Linux operating platform. Application programming interface and Google text-to-speech (TTS) are required as additional software packages to design this prototype system. Transform any Wikipedia pages into audio files through Wikitrola for libraries and information centers. Wikipedia articles are directly transformed into audio, as these integrate the content seamlessly for the user experience. The whole system has been designed and configured on the basis of machine learning to provide dynamic services among the readers. Findings The viewer could use the machine learning system to turn Wikipedia articles into audio files, allowing them to listen to Wikipedia content in audio format. This would make information more accessible and adaptable to diverse learning modes, allowing written content to be engaged in novel and visionary ways. Originality/value The insightful observation in connection with the paper is that it shows how to convert text-based material into audio through the Google TTS and machine learning Python programming and finally incorporate them in Wikipedia articles. A harmonious system of information dissemination and technical education is established. This approach shows the effectiveness of imagination and the use of programming tools to enhance learning and knowledge-seeking processes.

Exploring Learning Outcomes of Wikipedia-Based Assignments in an International Setting: A Case From an Undergraduate Public Administration Course in Taiwan

Wikipedia-based assignments have been adopted successfully in classrooms to improve learning outcomes. However, although this teaching method has been adopted regularly in Western higher education settings, more is needed in an international learning environment. Therefore, this research investigates how employing technology-mediated student-generated Wikipedia content can enhance learning outcomes in an international setting. This study adopted Wikipedia’s online user-generated content platform to create student-generated content in introductory Public Administration classes. We conducted quantitative and qualitative analyses to assess whether a Wikipedia writing assignment increased learning outcomes. Through pre-and post-surveys, students reported increased awareness of their English writing ability, knowledge of Wikipedia content writing, and knowledge of Wikipedia content writing. The study recommends adopting Wikipedia for introductory courses, especially relevant for courses taught in an international environment where English is the language of tuition. Nonetheless, instructors should act as active facilitators in group work, aid students in finding and shaping project topics, and narrow the project scope to ensure that it remains educational within the taught context. Furthermore, it is recommended to have a reasonable project length, design cross-group interactions, and include peer review and technical support in designing the project.

CFEVER: A Chinese Fact Extraction and VERification Dataset

We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as “Supports”, “Refutes”, or “Not Enough Info” to depict its degree of factualness. Similar to the FEVER dataset, claims in the “Supports” and “Refutes” categories are also annotated with corresponding evidence sentences sourced from single or multiple pages in Chinese Wikipedia. Our labeled dataset holds a Fleiss’ kappa value of 0.7934 for five-way inter-annotator agreement. In addition, through the experiments with the state-of-the-art approaches developed on the FEVER dataset and a simple baseline for CFEVER, we demonstrate that our dataset is a new rigorous benchmark for factual extraction and verification, which can be further used for developing automated systems to alleviate human fact-checking efforts. CFEVER is available at https://ikmlab.github.io/CFEVER.

Explaining how Wikipedia deals with credibility to university students: the case of Wikipedia in the Basque language

PurposeThis study aims to analyse how first-year undergraduate audiovisual communication students assess the credibility and reliability of information on Wikipedia in Basque and analyse whether the experience of being a Wikipedia editor helps to improve students’ perceptions of Wikipedia’s trustworthiness. The purpose of this project was to help students to better understand the credibility mechanisms of Wikipedia in Basque regarding the work of editing and creating entries. Do students’ perceptions about how reliable Wikipedia is change if they receive training as Wikipedia content editors and understand Wikipedia’s credibility mechanisms? What are their perceptions about the credibility of Wikipedia?Design/methodology/approachThe authors observed and analysed students’ behaviour and perceptions when creating Wikipedia articles and learning about the process of evaluating Wikipedia. This research was carried out at [masked for review] over four consecutive years, where 188 students in the Degree Programme in Audiovisual Communication participated through a questionnaire and in-depth interviews.FindingsAfter the experience of creating content on Wikipedia and learning about Wikipedia’s credibility mechanisms experientially, the majority of students reported that their perception of Wikipedia’s credibility had increased, and 70% of students believed Wikipedia to be reliable or more reliable than expected. A smaller number of students stated that understanding Wikipedia’s editing processes decreased their perception of Wikipedia’s credibility.Practical implicationsThis project will lead to a better understanding of the performance of students in editing content on Wikipedia and of the impact that this editing experience has on students’ perceptions of credibility with respect to Wikipedia. It will make it possible to better incorporate Wikipedia content editing into the learning processes of students and citizens. This study provides interesting keys to improving the reliability and credibility of Wikipedia among students.Social implicationsAs the study examined Basque Wikipedia, it has important social implications since Basque is a minority language and Wikipedia is the main sources of information for students who study in Basque.Originality/valueThe authors have been analysing the credibility of information in general and that of information on Wikipedia in particular since 2010. Based on this experience, the authors decided to design this study so that it was carried out while the students were creating and editing Wikipedia content as part of a project in their educational curriculum. Furthermore, the study expands the body of research carried out to date by incorporating the perspectives of media studies students.

Wikipedia and the Outsider Within: Black Feminism and Social Inequality in Knowledge Sharing

This paper examines the politics of knowledge on Wikipedia through a Black feminist lens, with particular attention to Patricia Hill Collins’s concept of Black women as “the outsider within” in intellectual spaces. We present an assignment in which a class of predominantly Black, female undergraduate students were tasked with analyzing and then improving content on Wikipedia. Wikipedia strives to be unbiased through a transparent writing and editing process that draws on reliable, published sources. These protocols regularly help catch and fix hoaxes and content vandalism. Nonetheless, we build on existing scholarship to show that Wikipedia has other kinds of biases that result in racist and sexist knowledge gaps, euphemisms, stereotypes, and misrepresentation. These problems are a result of (1) the personal experiences and opinions of Wikipedia editors, who are predominantly white and male; (2) the requirement for subjects to be deemed “noteworthy” through citing multiple sources that meet Wikipedia’s standards of reliability; and (3) gatekeeping practices by the existing editors. As a result, we argue that Wikipedia can not only extend but also exacerbate pro–white male biases present in the source materials that Wikipedia draws on. We note the potential for more diverse editors to improve Wikipedia content, but we also offer cautionary observations on this strategy. Last, we suggest that college instructors can teach students to better understand racialized and gendered knowledge processes through assignments to contribute to Wikipedia that are paired with supportive readings.

Leveraging Wikipedia in undergraduate health sciences education: a key tool for information literacy and knowledge translation.

Academic institutions and libraries are familiar with Wikipedia. There is growing momentum in higher education for using Wikipedia as a learning tool in various contexts. These include, but are not limited to, the use of Wikipedia-based assignments to teach information literacy, science communication, evidence-based practice, and more. Although there is growing acceptance of Wikipedia's value in the classroom, there are limited exemplars available for how it is applied in undergraduate health sciences education. This program description describes a librarian instructed course in the Bachelor of Health Sciences Program at McMaster University in which students dedicate one academic term to learning about Wikipedia content production and making contributions to a health-related Wikipedia article of their choice. In the five iterations of this course that have been offered, undergraduate health sciences students have made significant contributions to 25 health-related articles in Wikipedia. They have added more than 120,000 words and over 2,000 references to high-quality literature. In class, conversations emerged about the meaningfulness of the editing Wikipedia, information literacy, and knowledge translation.

Wikipedia and academia: University faculty patterns of use and perceptions of credibility

Wikipedia is a widely used information source among students in higher education institutes but is regarded as less credible among teaching faculty. The present study is an attempt to understand how faculty perceive, use and believe Wikipedia as a credible source of information. The paper draws upon a survey data gathered from 257 university faculty members by employing the systematic random sampling technique. The analysis of collected data suggests that faculty members used Wikipedia for multiple purposes ranging from teaching and research to leisure reading. They regarded Wikipedia as a useful and credible source of information while having a positive belief in the Wikipedia project. Overall, the findings of this study suggest that faculty perceptions have grown more favorable regarding Wikipedia use, usefulness, and quality of information. Hence, our study found that faculty members were trying to integrate Wikipedia into their teaching, learning and research endeavors by allowing students to consult and cite Wikipedia information and enabling them to be critical and proficient users of Wikipedia as a part of their information-seeking, gathering, and sense-making practices. However, there is a need of moving beyond the passive consumption of content to contribute to Wikipedia content by creating new entries, and adding to and editing earlier entries. The present study would enhance our understanding of the richness, usefulness, and credibility of Wikipedia information among university faculty from a developing country’s perspective. Such understanding contributes to illustrating the global acceptance level of Wikipedia in academia as a source of information and knowledge.

“Gua means scrape”: a conversation analysis of identity construction and negotiation in polylogal Wikipedia paratext

ABSTRACT This article contributes to multidisciplinary research exploring the role played by paratext in the construction of identities. First, it develops a theoretical and methodological framework inspired by recent scholarship on conducting conversation analysis in digital contexts and on the ways in which speakers claim positions of expertise through their turns-at-talk. Next, it presents a case study examining translation-focused discussion forum comments posted within the so-called “Talk” pages that surround the mainspace content of the online user-generated encyclopedia Wikipedia. The analysis highlights the often intensely interactive nature of digital paratext and probes the implications for participants’ identity work: I argue that the polylogal nature of this paratextual space requires the constant negotiation of identities as Wikipedia contributors relentlessly jostle with one another for recognition of their epistemic authority. In the final section, I discuss the implications of this Wikipedia case for broader areas of scholarship on translation and digital paratext.

Comprehensive Readability Assessment of Scientific Learning Resources

Readability is the measure of how easier a piece of text is. Readability assessment plays a crucial role in facilitating content writers and proofreaders to receive guidance about how easy or difficult a piece of text is. In literature, classical readability, lexical measures, and deep learning based model have been proposed to assess the text readability. However, readability assessment using machine and deep learning is a data-intensive task, which requires a reasonable-sized dataset for accurate assessment. While several datasets, readability indices (RI) and assessment models have been proposed for military agencies manuals, health documents, and early educational materials, studies related to the readability assessment of computer science literature are limited. To address this gap, we have contributed Computer science (CS) literature dataset <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AGREE</b> , comprising 42,850 learning resources(LR). We assessed the readability of learning objects(LOs) pertaining to domains of Computer Science (CS), machine learning (ML), software engineering (SE), and natural language processing (NLP). LOs consists of research papers, lecture notes and Wikipedia content of topics list of learning repositories for CS, NLP, SE and ML in English Language. From the statistically significant sample of LOs two annotators manually annotated LO’s text difficulty and established gold standard. Text readability was computed using 14 readability Indices (RI) and 12 lexical measures (LM). RI were ensembled, and readability measures were used to train the model for readability assessment. The results indicate that the extra tree classifier performs well on the AGREE dataset, exhibiting high accuracy, F1 score, and efficiency. We observed that there is no consensus among readability measures for shorter texts, but as the length of the text increases, the accuracy improves. The AGREE and SELRD datasets, along with the associated readability measures, provide a novel contribution to the field. They can be used to train deep learning models for readability assessment, develop recommender systems, and assist in curriculum planning within the domain of Computer Science. In the future, we plan to scale AGREE by adding more LOs and adding multimedia LOs. In addition, we would explore the use of deep learning methods for improved readability assessment.

Wikipedia searches and the epidemiology of infectious diseases: A systematic review

This review aims to collect, analyse and synthesize the available evidence that can be provided by Wikipedia for epidemiologic surveillance purposes. PRISMA guidelines were followed. PubMed/Medline and Scopus were consulted. Out of 238 retrieved articles, 16 articles were included in the systematic review. The most frequently assessed infectious disease was Influenza, followed by arboviruses and measles. Influenza studies show that Wikipedia could be considered a scientifically valid surveillance system that fills the main gaps in existing traditional surveillance systems. As regards arboviruses, searches on the Web have positively mediated the relationship between epidemiological data and the number of Wikipedia page visualization. Regarding measles, studies showed a strong/moderate temporal correlation between infectious disease notification bulletins and Wikipedia search trends. Despite the type of infectious agents, three main aims can be detected: (i) understand the public’s interest, (ii) explore the use of Wikipedia by organizations, and (iii) assess the accuracy of Wikipedia content. These new strategies for surveillance of infectious diseases should be implemented, to date they could be useful in supporting traditional surveillance.

Asian American Female Composers and Digital Memory

Asian American Female Composers and Digital Memory

Forms of Adaptation of Newcomers in Wikipedia as Online Sharing Knowledge Project

Abstract: Wikipedia is one of the largest virtual communities of practice aimed at building and sharing knowledge online. Its ambitious goals require the cooperation of millions of users around the world. The development of the project has slowed down over the last few years, hence the acquisition and retention of new users is becoming more and more challenging. Consequently, properly organized adaptation, and encouragement of newcomers to become a part of the community emerges as a crucial issue for the existence of Wikipedia.&#x0D; This paper presents the results of the quantitative research carried out with the use of the content analysis method on the different language versions of Wikipedia. The issues investigated included the following aspects: the level of advancement of the newcomers’ adaptation process, the use of various adaptation tools, forms of welcoming and enhancing newcomers to learn and edit the Wikipedia content, while also their potential effect on the productivity and engagement of users.&#x0D; The results of the research indicate great diversity among the tools used, depending on the language version of the project. Not all the statements directed to the newcomers are encouraging and friendly, as some cultures create more restrictive and punitive content than the others. The latter leaves the newcomer with the responsibility for the process of learning the rules of behaviour of the Wikipedia community. The more advanced the adaptation process is, the more active and productive users are. The conclusions of the research may be of use to different virtual communities of practice and organizations which face the problem of adapting newcomers in the online activity connected with knowledge sharing.

TWikiL – the Twitter Wikipedia Link Dataset

Recent research has shown how strongly Wikipedia and other web services or platforms are connected. For example, search engines rely heavily on surfacing Wikipedia links to satisfy their users' information needs and volunteer-created Wikipedia content frequently gets re-used on other social media platforms like Reddit. However, publicly accessible datasets that enable researchers to study the interrelationship between Wikipedia and other platforms are sparse. In addition to that, most studies only focus on certain points in time and don't consider the historical perspective. To begin solving these problems we developed TWikiL, the Twitter Wikipedia Link Dataset, which contains all Wikipedia links posted on Twitter in the period 2006 to January 2021. We extract Wikipedia links from Tweets and enrich the referenced articles with their respective Wikidata identifiers and Wikipedia topic categories, which will make this dataset immediately useful for a large range of scholarly use cases. In this paper, we describe the data collection process, perform an initial exploratory analysis and present a comprehensive overview of how this dataset can be useful for the research community.

Dataset of first appearances of the scholarly bibliographic references on Wikipedia articles

Referencing scholarly documents as information sources on Wikipedia is important because it supports or improves the quality of Wikipedia content. Several studies have been conducted regarding scholarly references on Wikipedia; however, little is known of the editors and their edits contributing to add the scholarly references on Wikipedia. In this study, we develop a methodology to detect the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable as the “first appearance of the scholarly reference.” We identified the first appearances of 923,894 scholarly references (611,119 unique DOIs) in 180,795 unique pages on English Wikipedia as of March 1, 2017 and stored them in the dataset. Moreover, we assessed the precision of the dataset, which was highly precise regardless of the research field. Finally, we demonstrate the potential of our dataset. This dataset is unique and attracts those who are interested in how the scholarly references on Wikipedia grew and which editors added them.

How well does online information-seeking behavior indicate public conservation orientation? Taxonomy and personal characteristics matter

Public conservation support for a species can powerfully influence the success of conservation policies and actions. To maximize the effectiveness of the limited resources available for conservation, it would be beneficial to develop a way to predict the degree of public conservation orientation. Here, we explore the utility of the frequency of accessing web content featuring particular species, a direct measure of information-seeking behavior, to predict public conservation orientation. Specifically, we tested whether pageviews of Wikipedia content featuring 100 threatened species in five taxonomic groups (mammals, birds, reptiles and amphibians, insects, and plants) is associated with public conservation orientation toward these species in Japan. Wikipedia pageviews predicted public conservation orientation for species conservation for the two most salient taxonomic groups (mammals and birds). This relationship, however, was not evident for the other three taxa. The relationship between Wikipedia pageviews and conservation orientation was influenced by respondent age and gender. We employed the national red list category of the species as a covariate, but it was not associated with public orientation for species conservation in any of the five taxonomic groups. Overall, information-seeking behavior could be used as a proxy for public conservation orientation for mammals and birds, but should be interpreted with caution.

Wikipedia at the University: Engaging Students and Teachers in Open Knowledge and Collaborative Work

In the last decade, Wikipedia has gone from being one of the great enemies of education to becoming a great ally, especially thanks to the degree of rigor and organization it has achieved. The free encyclopedia is increasingly present in universities and offers numerous opportunities for teachers for educational innovation. This paper describes an innovation project promoted by MediaLab USAL at the University of Salamanca and aimed at the generation and publication of open knowledge, at university level, in the open encyclopedia Wikipedia. This project aims to promote the creation of quality content in Spanish at the University, to include the rigorous editing of content in Wikipedia as part of the practical work of undergraduate and postgraduate subjects at the University of Salamanca and to offer a new free learning tool to teachers and students. The initiative has counted with the participation of more than 100 professors, 584 students and has achieved the creation of 1752 articles specialized in different areas of knowledge in Wikipedia.

The Wikipedia Diversity Observatory: helping communities to bridge content gaps through interactive interfaces

In this paper, we present the Wikipedia Diversity Observatory, a project aimed to increase diversity within Wikipedia content. The project provides dashboards with visualizations and tools which show content gaps in terms of imbalances in the coverage of topics, and of concepts that are not shared across Wikipedia language editions. The dashboards are built on datasets generated for each of the more than 300 existing language editions, with features that label each article according to geography, gender and other categories relevant to overall content diversity. Through various examples, we show how the tools encourage and help editors to bridge the gaps in Wikipedia content. Finally, we discuss the project’s impact on the communities and implications for the Wikimedia movement in a moment in which covering diversity is considered strategic.

Measuring Quality of Wikipedia Articles by Feature Fusion‐based Stack Learning

AbstractOnline open‐source knowledge repository such as Wikipedia has become an increasingly important source for users to access knowledge. However, due to its large volume, it is challenging to evaluate Wikipedia article quality manually. To fill this gap, we propose a novel approach named “feature fusion‐based stack learning” to assess the quality of Wikipedia articles. Pre‐trained language models including BERT (Bidirectional Encoder Representations from Transformers) and ELMo (Embeddings from Language Models) are applied to extract semantic information in Wikipedia content. The feature fusion framework consisting of semantic and statistical features is built and fed into an out‐of‐sample (OOS) stacking model, which includes both machine learning and deep learning models. We compare the performance of proposed model with some existing models with different metrics extensively, and conduct ablation studies to prove the effectiveness of our framework and OOS stacking. Generally, the experiment shows that our method is much better than state‐of‐the‐art models.

It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia

Wikipedia is a community-created encyclopedia that contains information about notable people from different countries, epochs and disciplines and aims to document the world's knowledge from a neutral point of view. However, the narrow diversity of the Wikipedia editor community has the potential to introduce systemic biases such as gender biases into the content of Wikipedia. In this paper we aim to tackle a sub problem of this larger challenge by presenting and applying a computational method for assessing gender bias on Wikipedia along multiple dimensions. We find that while women on Wikipedia are covered and featured well in many Wikipedia language editions, the way women are portrayed starkly differs from the way men are portrayed. We hope our work contributes to increasing awareness about gender biases online, and in particular to raising attention to the different levels in which gender biases can manifest themselves on the web.

Collaborative Visualizations for Wikipedia Critique and Activism

Wikipedia is one of the largest platforms based on the concept of asynchronous, distributed, collaborative work. A systematic collaborative exploration and assessment of Wikipedia content and coverage is however still largely missing. On the one hand editors routinely perform quality and coverage control of individual articles, while on the other hand academic research on Wikipedia is mostly focused on global issues, and only sporadically on local assessment. In this paper, we argue that collaborative visualizations have the potential to fill this gap, affording editors to collaboratively explore and analyse patterns in Wikipedia content, at different scales. We illustrate how a collaborative visualization service can be an effective tool for editors to create, edit, and discuss public visualizations of Wikipedia data. Combined with the large Wikipedia user-base, and its diverse local knowledge, this could result in a large-scale collection of evidence for critique and activism, and the potential to enhance the quantity and quality of Wikipedia content.