Wikipedia Research Articles

Architectural styles of curiosity in global Wikipedia mobile app readership.

Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we analyze a naturalistic population of 482,760 readers using Wikipedia's mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we replicate two styles of curiosity previously identified in laboratory studies: the nomadic "busybody" and the targeted "hunter." Further, we find evidence for another style-the "dancer"-which was previously predicted by a historico-philosophical examination of texts over two millennia and is characterized by creative modes of knowledge production. We identify associations, globally, between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. These results advance our understanding of Wikipedia's global readership and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity.

The influence of spider news on online information-seeking.

Fear of spiders is a widespread condition often disproportionate to the actual danger spiders pose to humans. Likely rooted in evolutionary history, fear of spiders might also have a cultural component. Recent studies have shown that a significant fraction of spider-related media reports are misleading and sensationalistic. Information-seeking behaviours serve as common coping mechanisms for our fears and anxieties, yet the link between spider-related news stories and such behaviors remains unexplored.We hypothesize that media reports foster concern about spiders, resulting in an increased awareness of spiders and health issues associated with them. We extracted 1486 reports in English from a public database providing a content-analysis of spider-related online traditional media reports published between 2010-2020. We examined whether the volume of spider-related queries in Google Trends, Wikipedia, and iNaturalist increased in the week following the publication of each news story.Sensationalistic news stories were associated with a small, significant increase in search volumes, compared to non-sensationalistic ones. The search volume for "brown recluse" (Loxosceles reclusa), which are potentially dangerous spiders, was higher after the publication date of news related to human-spider encounters. There was a significant positive relationship between the number of spider-related news stories published in a given month and the traffic on target spider-related Wikipedia pages, especially so for the page on brown recluse spiders.Our results suggest that traditional media have a detectable impact on the behaviour of the general public towards spiders, supporting the hypothesis that the fear of spiders is perpetuated by culture. Additionally, our findings indicate that information-seeking behaviour is a common response to learn about spiders and potentially fact-check spurious claims found in sensationalised news. By recognizing the role of media in shaping attitudes towards spiders and acknowledging the benefits of accurate representation, we can lay the foundation for a more informed and harmonious relationship between humans and spiders.

Mapping and modeling the semantic space of math concepts

Mathematics is an underexplored domain of human cognition. While many studies have focused on subsets of math concepts such as numbers, fractions, or geometric shapes, few have ventured beyond these elementary domains. Here, we attempted to map out the full space of math concepts and to answer two specific questions: can distributed semantic models, such a GloVe, provide a satisfactory fit to human semantic judgements in mathematics? And how does this fit vary with education? We first analyzed all of the French and English Wikipedia pages with math contents, and used a semi-automatic procedure to extract the 1000 most frequent math terms in both languages. In a second step, we collected extensive behavioral judgements of familiarity and semantic similarity between them. About half of the variance in human similarity judgements was explained by vector embeddings that attempt to capture latent semantic structures based on cooccurence statistics. Participants' self-reported level of education modulated familiarity and similarity, allowing us to create a partial hierarchy among high-level math concepts. Our results converge onto the proposal of a map of math space, organized as a database of math terms with information about their frequency, familiarity, grade of acquisition, and entanglement with other concepts.

Assessing the political vulnerability of National Parks in sub‐Saharan Africa using data on digital trends and engagement

Abstract National Parks (NPs) and other protected areas in sub‐Saharan Africa are crucial in attracting international tourists interested in wildlife tourism, contributing to national economies. Despite their cultural, economic, and conservation significance, these areas face diverse development threats, leading to pressures for protected area downgrading, downsizing, or degazettement (known as PADDD). We comprehensively analyse the geographical, historical, and structural aspects of 322 NPs in sub‐Saharan Africa and assess their political vulnerability by exploring the interplay of development pressures and public interest (as measured through Wikipedia page views). Fewer than 30% of these NPs possess or report information regarding management plans, even among the five most frequently viewed areas on Wikipedia. This is particularly concerning, since among those who underwent a PADDD event (51 NPs), almost 90% of them also had no information about the existence of management plans. Although we did not identify a statistically significant association between tourism and public interest online, tourism emerges as a potential mitigating factor against PADDD, along with high levels of peace. NPs that garner significant public interest online do not share the same socio‐geographic profile as parks that are most resilient to PADDD events. While digital metrics of public interest have potential as indicators of political resilience in NP, our research has shown that these metrics need refinement to fully understand which biophysical and cultural aspects of parks attract more public attention. Overall, effective conservation strategies in sub‐Saharan Africa require a more holistic understanding of historical, socio‐economic, and cultural factors. Read the free Plain Language Summary for this article on the Journal blog.

Strategies for crowdsourcing hearing health information: a comparative study of educational programs and volunteer-based campaigns on Wikimedia

BackgroundSeveral health institutions developed strategies to improve health content on Wikimedia platforms given their unparalleled reach. The objective of this study was to compare an online volunteer-based Wikimedia outreach campaign with university course Wikipedia assignments (both focused on improving hearing health content in Wikimedia’s public digital knowledge archives), in terms of the reach of the contributions and the extent of the participants’ input. A secondary objective was to examine the feasibility and the implementation of the different strategies.MethodsThe research team partnered for the (1) coordination of improvements in hearing and healthcare content through educational programs using Wikimedia platforms, (2) participation in the global campaign Wiki4WorldHearingDay2023 and (3) evaluation of the proposed strategies. Metrics used in the comparison of the two strategies included the number of articles edited, number of views of the edited articles (as reach) and the extent of edits, captured as the number of words. The feasibility evaluation included assessing recruitment success and the implementation of the proposed plan among faculty, students from various university programs, and volunteers representing different constituencies.ResultsThe effort increased the availability of quality plain language information on hearing conditions and hearing care. Both strategies demonstrated to be feasible by their success in recruiting participants who contributed to the effort and by measurable outputs as edits. The contribution of content to Wikimedia platforms as part of education activities provided a more robust result. Wiki4WorldHearingDay2023 145 participants (78 from educational programs) contributed 167,000 words, 258 + references and 140 images to 322 Wikipedia articles (283 existing and 39 new ones), which were viewed 16.5 million times. Contributions occurred in six languages. Edits in Portuguese, mainly by those involved in educational programs, led the number of articles (226 or 70.2%) that were expanded or created during the 5-month tracking period.ConclusionsThe elements that contributed to the success of the studied strategies include an impact topic, coordination with educational programs, international multidisciplinary collaborations, the dissemination of the initiative in several platforms, connection with a robust local Wikimedia affiliate, and the use of a technical infrastructure that provides metrics and coordination mechanisms.Graphical abstract

Quality of Male and Female Medical Content on English-Language Wikipedia: Quantitative Content Analysis.

Wikipedia is the largest free online encyclopedia and the seventh most visited website worldwide, containing >45,000 freely accessible English-language medical articles accessed nearly 1.6 billion times annually. Concerns have been expressed about the balance of content related to biological sex on Wikipedia. This study aims to categorize the top 1000 most-read (most popular) English-language Wikipedia health articles for June 2019 according to the relevance of the article topic to each sex and quality. In the first step, Wikipedia articles were identified using WikiProject Medicine Popular Pages. These were analyzed on 13 factors, including total views, article quality, and total number of references. In the second step, 2 general medical textbooks were used as comparators to assess whether Wikipedia's spread of articles was typical compared to the general medical coverage. According to the article's content, we proposed criteria with 5 categories: 1="exclusively female," 2="predominantly female but can also affect male individuals," 3="not sex specific or neutral," 4=predominantly male but can affect female individuals," and 5="exclusively male." Of the 1000 Wikipedia health articles, 993 (93.3%) were not sex specific and 67 (6.7%) were sex specific. There was no statistically significant difference in the number of reads per month between the sex-specific and non-sex-specific articles (P=.29). Coverage of female topics was higher (50/1000, 5%) than male topics (17/1000, 1.7%; this difference was also observed for the 2 medical textbooks, in which 90.2% (2330/2584) of content was not sex specific, female topics accounted for 8.1% (209/2584), and male topics for accounted for 1.7% (45/2584; statistically significant difference; Fisher exact test P=.03). Female-category articles were ranked higher on the Wikipedia medical topic importance list (top, high, or mid importance) than male-category articles (borderline statistical significance; Fisher exact test P=.05). Female articles had a higher number of total and unique references; a slightly higher number of page watchers, pictures, and available languages; and lower number of edits than male articles (all were statistically nonsignificant). Across several metrics, a sample of popular Wikipedia health-related articles for both sexes had comparable quality. Wikipedia had a lower number of female articles and a higher number of neutral articles relative to the 2 medical textbooks. These differences were small, but statistically significant. Higher exclusively female coverage, compared to exclusively male coverage, in Wikipedia articles was similar to the 2 medical textbooks and can be explained by inclusion of sections on obstetrics and gynecology. This is unlike the imbalance seen among biographies of living people, in which approximately 77.6% pertain to male individuals. Although this study included a small sample of articles, the spread of Wikipedia articles may reflect the readership and the population's content consumption at a given time. Further study of a larger sample of Wikipedia articles would be valuable.

Declaring crisis? Temporal constructions of climate change on Wikipedia.

On Wikipedia, editors daily negotiate edits to an entry that summarizes climate change to a global audience. The outcome of their efforts is an encyclopedic entry with a conspicuous lack of temporal clarity that circumvents the question of whether climate change is an immediate crisis or merely a potential future phenomenon. This qualitative discourse analysis of editors' debates around climate change on Wikipedia argues that their hesitancy to "declare crisis" is not a conscious editorial choice as much as an outcome of a friction between the folk philosophy of science Wikipedia is built upon, editors' own sense of urgency, and their anticipations about audience uptake of their writing. This friction shapes a group style that fosters temporal ambiguity. Hence, the findings suggest that in the Wikipedia entry on climate change, platform affordances and contestation of expertise foreclose a declaration of climate crisis.

Assembling Hyperpop: Genre Formation on Wikipedia

In this article, we analyze how a music genre is reciprocally defined online, through the crowdsourced ‘facts’ of Wikipedia. What are the traditional implicit and explicit exclusionary practices in this sort of genre formation? And how can internet-specific sociabilities create new exclusionary practices in the process of defining music? By analyzing the edit history of Wikipedia’s ‘hyperpop’ page, we locate ongoing debates, controversies, and contestations that point to shaping forces around online genre formation. These potentially have a huge impact on how hyperpop is understood both inside and outside of the music community. In locating the most active editors of the hyperpop Wikipedia page and scrutinizing their edit histories as well as the discussions on the hyperpop page itself, we uncovered debates about artistic notability, biases toward specific sources, and attempts at associating or dissociating musical genre from non-musical identities (such as race, gender, and nationality). In sum, we aimed to investigate the main controversies involved in the Wikipedia definition of hyperpop for what they can tell us about both the process of online genre formation and the very nature of collective knowledge-making spaces.

Polarized collaboration benefits knowledge production: empirical analyses of the mediating effect of co-production pattern in Wikipedia articles on climate change

Polarized collaboration benefits knowledge production: empirical analyses of the mediating effect of co-production pattern in Wikipedia articles on climate change

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.

Serial Murderers In Japan (1882-2017): A Narrative Analysis

A retrospective narrative analysis of 61 serial murder-ers (a few, belonging to health care profession, such as physicians, midwives and paramedic) in Japan was attempted. Materials available in the online databases including Wikipedia entries on individual criminals were studied. Among the studied total of 61 serial murderers covering the time span from 1882 to 2017, 59 were convicted and 2 were non-convicted. All five categories of Deitz typology of serial murderers were represented among the convicted sample. These include, psychopathic sex-ual sadists (e.g: Ryuun Daimai, Yoshio Kodaira, Genzo Kurita, Kiyoshi Okubo, Takahiro Shiraishi), crime spree killers (e.g: Kanae Kijima, Chisako Kakehi), custodial killers (e.g: Shige Sakakura, Miyuki Ishikawa and Hayato Imai), psychotic, religious-cult killers (e.g: Sachiko Eto, Shoko Asahara and his 12 collaborators) and organized crime functionaries (e.g: Satoru Nomu-ra). Tomomasa Nakagawa, personal doctor of the Aum Shinrikyo Buddhist religious-cult leader Shoko Asakara, may hold a record for a scientist on death row to publish a paper in a peer-reviewed journal.Limitations of this study include, (1) omission of the methods of killing used by the murderers from analysis (2) total reliance on media reports for information, in preference to voluminous records of Japanese court cases. Despite these limitations, details presented in this analysis of 59 convicted and 2 non-convicted se-rial murderers in 45 court cases held in Japan may be useful for comparative studies in forensic science and law enforcement.

How reproductive health information-seeking behaviors changed post-Dobbs: A Wikipedia page views analysis (Preprint)

How reproductive health information-seeking behaviors changed post-Dobbs: A Wikipedia page views analysis (Preprint)

Pasifika arts Aotearoa and Wikipedia

ABSTRACT This article is summary and reflections from Sophia Coghini and Lisa Maule on a pilot Wikipedia project in Aotearoa New Zealand in 2022. The pilot sought to examine out if Wikipedia was considered of value to a Pasifika community, to increase content about Pasifika arts in Wikipedia, and to teach Pasifika people to edit Wikipedia, Wikimedia Commons and Wikidata. The research describes the discoveries and reflections of people in the project. Difficulties for the new editors included a lack of writing about Indigenous artists in secondary sources to create Wikipedia articles. There was a questioning of how to bring Pasifika concepts of talanoa and Vā into a Wikipedia learning journey. The Pasifika arts communities of the project validated content to be in Wikipedia, but there was a sadness from older generations that notable artists were not easily able to be added.

A data-driven analysis to determine the optimal number of topics 'K' for latent Dirichlet allocation model

Topic modeling is an unsupervised machine learning technique successfully used to classify and retrieve textual data. However, the performance of topic models is sensitive to selecting optimal hyperparameters, the number of topics 'K' and Dirichlet priors 'α' and 'β.' This data-driven analysis aims to determine the optimum number of topics, 'K,' within the latent Dirichlet allocation (LDA) model. This work utilizes three datasets, namely 20-Newsgroups news articles, Wikipedia articles, and Web of Science containing science articles, to assess and compare various 'K' values through the grid search approach. The grid search approach finds the best combination of hyperparameter values by trying all possible combinations to see which performs best. This research seeks to identify the 'K' that optimizes topic relevance, coherence, and model performance by leveraging statistical metrics, such as coherence scores, perplexity, and topic distribution quality. Through empirical analysis and rigorous evaluation, this work provides valuable insights for determining the ideal 'K' for LDA models.

The Wikipedia effect: Analyzing investor attention for strategic investment decisions

The industries with the highest increase in Wikipedia page views are the telecommunications, consumer durables, and high-technology sectors. On average, firms with increasing views have higher returns, lower earnings per share, and higher price-to-earnings ratios. Moreover, long-short investment strategies using changes in Wikipedia page views yield profitable portfolio returns, outperforming the market. Factor model regressions demonstrate that these portfolios exhibit positive abnormal returns relative to various benchmark models. In particular, the high-minus-low portfolio shows statistically significant alphas that common risk factors cannot explain. The findings suggest that changes in firms’ Wikipedia page views are a valuable indicator of stock performance.

Enhancing toponym identification: Leveraging Topo-BERT and open-source data to differentiate between toponyms and extract spatial relationships

Abstract. Geoparsing, the process of linking locations within text to sets of geographic coordinates, plays an important role in the extraction and analysis of information from unstructured textual data. With the rapid growth in availability of user-generated data from online sources, there is increasing demand for reliable geoparsing methods. Central to many of these methods is the accurate identification of toponyms within text. For some applications, however, simple identification of toponyms is insufficient. Problems which require the association of a piece of text containing multiple toponyms to a singular location require a more nuanced approach. In this paper, we show that a transformer based deep learning model, is able to identify the subject toponym within a given text, and classify other toponyms in terms of their spatial relationship with the subject. We curate a dataset of text taken from Wikipedia pages representing 5252 locations, and use OpenStreetMap data to classify toponyms within the text in terms of their spatial relationship with the subject of each article. This dataset is then used to train a transformer based deep-learning model. On a human labelled test set, our model achieves an F1 score of 0.916 when identifying the subject toponym, and 0.884 and 0.793 when identifying toponyms representing parent and child locations of the subject, respectively. We also consider the more complex adjacent and crossing relationships - with the model achieving F1 scores of 0.548 and 0.704 in these categories, respectively.

GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents

Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents.

Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages

Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages

Disappearing without a Trace: Coverage, Community, Quality, and Temporal Dynamics of Wikipedia Articles on Endangered Brazilian Indigenous Languages

Disappearing without a Trace: Coverage, Community, Quality, and Temporal Dynamics of Wikipedia Articles on Endangered Brazilian Indigenous Languages

Citations in Wikipedia for understanding research reach.

Wikipedia is the most frequently accessed online health information resource and is well positioned as a valuable tool for public health communication and knowledge translation. The authors aimed to explore their institution's health and medical research reach by analyzing its presence in Wikipedia articles. In October 2022, a comprehensive database search was constructed in PubMed to retrieve clinical evidence syntheses published by at least one author affiliated with McMaster University from 2017 to 2022, inclusive. Altmetric Explorer was queried using PubMed Identifiers and article titles to access metadata and Wikipedia citation data. 3,582 health evidence syntheses from at least one McMaster University affiliated author were analyzed. Six percent (n=219) of health evidence syntheses from the authors' institution were cited 568 times in 524 unique Wikipedia articles across 28 different language editions. 45% of citations appeared in English Wikipedia, suggesting a broad global reach for the institutions' research outputs. When adjusted for open access publications, 8% of McMaster University's health evidence syntheses appear in Wikipedia. Altmetric Explorer is a valuable tool for exploring the reach of an institution's research outputs. Isolating Altmetric data to focus on Wikipedia citations has value for any institution wishing to gain more insight into the global, community-level reach of its contributions to the latest health and medical evidence.