Wikipedia Research Articles

Public Awareness of the Fencing Response as an Indicator of Traumatic Brain Injury: Quantitative Study of Twitter and Wikipedia Data.

Traumatic brain injury (TBI) is a disruption in normal brain function caused by an impact of external forces on the head. TBI affects millions of individuals per year, many potentially experiencing chronic symptoms and long-term disability, creating a public health crisis and an economic burden on society. The public discourse around sport-related TBIs has increased in recent decades; however, recognition of a possible TBI remains a challenge. The fencing response is an immediate posturing of the limbs, which can occur in individuals who sustain a TBI and can be used as an overt indicator of TBI. Typically, an individual demonstrating the fencing response exhibits extension in 1 arm and flexion in the contralateral arm immediately upon impact to the head; variations of forearm posturing among each limb have been observed. The tonic posturing is retained for several seconds, sufficient for observation and recognition of a TBI. Since the publication of the original peer-reviewed article on the fencing response, there have been efforts to raise awareness of the fencing response as a visible sign of TBI through publicly available web-based platforms, such as Twitter and Wikipedia. We aimed to quantify trends that demonstrate levels of public discussion and awareness of the fencing response over time using data from Twitter and Wikipedia. Raw Twitter data from January 1, 2010, to December 31, 2019, were accessed using the RStudio package academictwitteR and queried for the text "fencing response." Data for page views of the Fencing Response Wikipedia article from January 1, 2010, to December 31, 2019, were accessed using the RStudio packages wikipediatrend and pageviews. Data were clustered by weekday, month, half-year (to represent the American football season vs off-season), and year to identify trends over time. Seasonal regression analysis was used to analyze the relationship between the number of fencing response tweets and page views and month of the year. Twitter mentions of the fencing response and Wikipedia page views increased overall from 2010 to 2019, with hundreds of tweets and hundreds of thousands of Wikipedia page views per year. Twitter mentions peaked during the American football season, especially on and following game days. Wikipedia page views did not demonstrate a clear weekday or seasonal pattern, but instead had multiple peaks across various months and years, with January having more page views than May. Here, we demonstrated increased awareness of the fencing response over time using public data from Twitter and Wikipedia. Effective scientific communication through free public platforms can help spread awareness of clinical indicators of TBI, such as the fencing response. Greater awareness of the fencing response as a "red-flag" sign of TBI among coaches, athletic trainers, and sports organizations can help with medical care and return-to-play decisions.

Church of the Goat Man

Church of the Goat Man Kathleen Driskell (bio) 1 The Goat Man has his own Facebook page, Wikipedia page, short movie, entry in the Encyclopedia of Louisville, and is the subject of several poorly produced podcasts I have listened to recently. The Goat Man is also known as the Pope Lick Monster. Pope Lick is the shallow creek that meanders alongside the county road in Kentucky that we have lived on for decades, also named Pope Lick. When I rattle off my address, I usually get one of two reactions. Some snicker at the supposed bawdiness of my road’s name. To them, I explain that a “lick”—something between a rill and a stream—is a water source that emanates from a salt deposit and attracts all sorts of wild animals, making it a popular hunting spot. “Uh-huh,” they respond, as if to imply sure, whatever you say, lady. The other reaction is from those who already know something about the Norfolk Southern train trestle that towers over both Pope Licks, the creek and the road, and where the Goat Man lurks searching for his next victim. They may have even heard that the Goat Man has taken up residency with his wife and kids nearby in the old, abandoned Devil Church. If so, I might tease a bit, telling them that, actually, I am married to the Goat Man, which makes me Mrs. Goat Man, I suppose, though, honestly, no one has ever seemed that interested in hunting me down. [End Page 27] 2 At sixteen, I sat next to my mother in the local Methodist church, watched her finger slide across the words of the hymnal, her elbow in my ribs nudging me to sing along. I hadn’t found Keats yet, nor his poems about skepticism that would come to mean so much, but I was already bone sure her fundamentalist God wasn’t ever going to be more than a puff of superstition for me. Even in times of desperation, a child’s traumatic accident, my husband’s cancer, I’ve waved off any notion of that god as if it were cigar smoke in a crowded restaurant. Yet when I unlock the front door, enter my home through what once was the steeple with an actual brass bell hanging overhead and pass through my living room in what was the nave (what I suppose still is the nave), I am electric, charged by and grateful for the unwavering faith of those Lutherans who, at least a decade before the American Civil War, raised this building for worship. I feel more than lucky—I feel as if my family was meant to live here, in this church. That I was part of a rescue squad sent with my husband to save the beautiful old post and beam structure, its lumber harvested from nearby woods a hundred and forty years earlier, meant to save what had become a sad sack of a building by the time my husband and I stumbled upon it for sale in 1994. But what congregation—early or of late—could have imagined me schlepping through the nave with groceries, putting a head of romaine and a gallon of two percent milk away in the refrigerator, now sitting where we discovered a pulpit when our Realtor unlocked the double doors at the front of the church and stepped aside as my husband and I made our way in for the first time. 3 Nearly thirty years ago, I pushed past the Realtor, into the vestibule, and stood for a moment, gathering my senses before commencing warily, hands in front, feeling my way, blinded by having just come out of blazing sunlight. I almost turned my ankle on an empty wine bottle. Terry and I had been jabbering nonstop about the fabulous home we could make in the church since we discovered it while out house hunting. [End Page 28] Hopelessly lost, far beyond any of the Louisville neighborhoods we’d grown up in and running late for a showing for a home we already knew we couldn’t afford, we pulled over to get our bearings and realized we were in...

The Worldwide Utilization of Online Information about Dementia from 2004 to 2022: An Infodemiological Study of Google and Wikipedia

With the advent of Infodemics about health conditions, including dementia, nurses can engage in infodemiological studies to inform public health services and policies. This infodemiological study described the worldwide utilization of online information for dementia using Google Trends and Wikipedia page views. It found that the utilization of online information about dementia increased and will further increase using Google in succeeding years. Thus, the Internet is an increasingly important medium for dementia information in this age of misinformation and disinformation. Nurse informaticists can perform national infodemiological studies to inform and contextualize online dementia information. Likewise, public health, geriatric, and mental health nurses can collaborate with their communities and patients to address online disinformation and create culturally-appropriate information about dementia.

위키백과를 활용한 문서 주제 추출

Automatic text classification and topic extraction have received much attention from many fields, and various approaches for the problem have been proposed so far. In some of the proposed methods, a few ways of using Wikipedia data have been suggested and implemented. This paper presents a novel method to explore Wikipedia data to extract suitable topics from any given text document. The proposed method uses the network of Wikipedia articles and their categories to find most relevant Wikipedia categories for the given text. Finally, the method designates these most relevant categories as the topics of the document. An experimental result and the feasibility of the proposed method are illustrated.

Wikipedia’s Intentional Distortion of the History of the Holocaust

ABSTRACT This essay uncovers the systematic, intentional distortion of Holocaust history on the English-language Wikipedia, the world’s largest encyclopedia. In the last decade, a group of committed Wikipedia editors have been promoting a skewed version of history on Wikipedia, one touted by right-wing Polish nationalists, which whitewashes the role of Polish society in the Holocaust and bolsters stereotypes about Jews. Due to this group’s zealous handiwork, Wikipedia’s articles on the Holocaust in Poland minimize Polish antisemitism, exaggerate the Poles’ role in saving Jews, insinuate that most Jews supported Communism and conspired with Communists to betray Poles (Żydokomuna or Judeo–Bolshevism), blame Jews for their own persecution, and inflate Jewish collaboration with the Nazis. To explain how distortionist editors have succeeded in imposing this narrative, despite the efforts of opposing editors to correct it, we employ an innovative methodology. We examine 25 public-facing Wikipedia articles and nearly 300 of Wikipedia’s back pages, including talk pages, noticeboards, and arbitration cases. We complement these with interviews of editors in the field and statistical data gleaned through Wikipedia’s tool suites. This essay contributes to the study of Holocaust memory, revealing the digital mechanisms by which ideological zeal, prejudice, and bias trump reason and historical accuracy. More broadly, we break new ground in the field of the digital humanities, modelling an in-depth examination of how Wikipedia editors negotiate and manufacture information for the rest of the world to consume.

Supervised Contrastive Learning for Interpretable Long-Form Document Matching

Recent advancements in deep learning techniques have transformed the area of semantic text matching (STM). However, most state-of-the-art models are designed to operate with short documents such as tweets, user reviews, comments, and so on. These models have fundamental limitations when applied to long-form documents such as scientific papers, legal documents, and patents. When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of “similarity”), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content. In this article, we describe CoLDE : Co ntrastive L ong D ocument E ncoder—a transformer-based framework that addresses these challenges and allows for interpretable comparisons of long documents. CoLDE uses unique positional embeddings and a multi-headed chunkwise attention layer in conjunction with a supervised contrastive learning framework to capture similarity at three different levels: (i) high-level similarity scores between a pair of documents, (ii) similarity scores between different sections within and across documents, and (iii) similarity scores between different chunks in the same document and across other documents. These fine-grained similarity scores aid in better interpretability. We evaluate CoLDE on three long document datasets namely, ACL Anthology publications, Wikipedia articles, and USPTO patents. Besides outperforming the state-of-the-art methods on the document matching task, CoLDE is also robust to changes in document length and text perturbations and provides interpretable results. The code for the proposed model is publicly available at https://github.com/InterDigitalInc/CoLDE .

A New Query Expansion Approach for Improving Web Search Ranking

Information systems have come a long way in the 21st century, with search engines emerging as the most popular and well-known retrieval systems. Several techniques have been used by researchers to improve the retrieval of relevant results from search engines. One of the approaches employed for improving relevant feedback of a retrieval system is Query Expansion (QE). The challenge associated with this technique is how to select the most relevant terms for the expansion. In this research work, we propose a query expansion technique based on Azak & Deepak's WWQE model. Our extended WWQE technique adopts Candidate Expansion Terms selection with the use of in-links and out-links. The top two relevant Wikipedia articles from the user's initial search were found using a custom search engine over Wikipedia. Following that, we ranked further Wikipedia articles that are semantically connected to the top two Wikipedia articles based on cosine similarity using TF-IDF Vectorizer. The expansion terms were then taken from the top 5 document titles. The results of the evaluation of our methodology utilizing TREC query topics (126-175) revealed that the system with extended features gave ranked results that were 11% better than those from the system with unexpanded queries.

The history of women in engineering on Wikipedia

The history of women in engineering on Wikipedia

How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?

Natural language texts, such as tweets and news, contain a vast amount of geospatial information, which can be extracted by first recognizing toponyms in texts (toponym recognition) and then identifying their geospatial representations (toponym disambiguation). This paper focuses on toponym disambiguation, which can be approached by toponym resolution and entity linking. Recently, many novel approaches, especially deep learning-based, have been proposed, such as CamCoder, GENRE, and BLINK. However, these approaches were not compared on the same and large datasets. Moreover, there is still a need and space to improve their robustness and generalizability further. To mitigate the two research gaps, in this paper, we propose a spatial clustering-based voting approach combining several individual approaches and compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly challenging datasets (e.g., WikToR). They are in six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing 98,300 toponyms. Experimental results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving its generalizability and robustness. It also drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways. The detailed evaluation results can inform future methodological developments and guide the selection of proper approaches based on application needs.

Topic modelling to support English text selection for translation into South Africa's other official languages

Appropriate training data is a prerequisite for the development of natural language processing (NLP) techniques. Vast amounts of language data are typically required to develop NLP tools that perform at state-of-the-art level. Such abundant resources are currently only available in a few languages. The remaining languages have to find alternative ways to become ``NLP-enabled''. The aim of the study reported on here is to make more language data available to support NLP development in the official languages of South Africa. In this paper we present the idea of generating text data by means of translation. We also propose the use of topic modelling to identify text in a highly resourced source language that will yield meaningful translations in under-resourced target languages. More specifically, the paper describes how topic modelling was used to identify English Wikipedia articles that should be suitable for translation into South Africa's 10 other official languages.

PQuAD: A Persian question answering dataset

We present the Persian Question Answering Dataset (PQuAD), a crowdsourced reading comprehension dataset on Persian Wikipedia articles. It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine various properties of the dataset to show the diversity and the level of its difficulty as a MRC benchmark. By releasing this dataset, we aim to ease research on Persian reading comprehension and the development of Persian question answering systems. Our experiments on different state-of-the-art pre-trained contextualized language models show 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.

Using Google Trends and Wikipedia to Investigate the Global Public's Interest in the Pancreatic Cancer Diagnosis of a Celebrity.

A cross-sectional study was designed to assess the impact of a celebrity's announcement of having been diagnosed with pancreatic cancer on the volume of cancer-related research on the Internet. Global searches were carried out on Google Trends (GT) for the period from 1 January 2004 to 20 November 2022 (since data prior to 2004 were not available) using the search words Tumore del Pancreas (pancreatic cancer), Tumore neuroendocrino (neuroendocrine tumor), and Fedez (the name of a popular Italian rapper). The frequency of specific page views for Fedez, Tumore del pancreas, and Tumore neuroendocrino was collected via Wikipedia Trends data. Statistical analyses were carried out using the Pearson correlation coefficient (r). The GT data revealed a strong correlation (r = 0.83) while the Wikipedia Trends data indicated a moderate correlation (r = 0.37) for Tumore neuroendocrino and Tumore del pancreas. The search peaks for the GT and Wikipedia pages occur during the same time period. An association was found between the celebrity's announcement of his pancreatic cancer diagnosis and the volume of pancreatic-cancer-related online searches. Our findings demonstrate that media events and media coverage of health-related news can raise people's curiosity and desire for health information.

Introducing an “invisible enemy”: A case study of knowledge construction regarding microplastics in Japanese Wikipedia

Microplastics, a novel global environmental problem, has become a fast-developing research field with worldwide investments. With scientific development, social science studies are expected to contribute to developing more effective communication on microplastics. Among various media, this article focused on Wikipedia and discussed one particular situation—when the language of Wikipedia differs from the language of mainstream information sources. Inspired by actor network theory, we analyzed the revision history of the Japanese Wikipedia article on microplastics from 2014 to 2020 and elucidated how knowledge of microplastics was textually constructed. We found editors’ reluctance to disprove and update the latest scientific knowledge in edit actions, as well as problems and flaws existing in the created knowledge that shows limited enforcement of Wikipedia policies. Based on the findings, we urge more attention to improving the public knowledge of fast-developing science, especially when the knowledge construction might encounter a cross-language context.

Understanding the Use of Scientific References in Multilingual Wikipedia across Various Topics

To maintain information quality, Wikipedia articles are required to be based on reliable sources that readers can verify. However, assessing the reliability of sources is subjective and can vary depending on the language and topic. As a result, different language versions of Wikipedia may have their own criteria for determining the credibility of sources. Some Wikipedia references point to scientific sources that are generally considered more reliable than websites because they are subject to a rigorous peer review process and are published by reputable academic publishers. This means that the information presented in scientific sources has been thoroughly evaluated by experts in the relevant field, ensuring a higher degree of accuracy and credibility. In this study, we analyzed 325 million references from 60 million Wikipedia articles in 310 language versions to find scientific sources of information. Additionally, we divided the articles into different topics using three approaches: Wikipedia Projects, Wikidata and DBpedia. This allowed us to find and compare Sci score of each language version of Wikipedia and the topic.

“Too Soon” to count? How gender and race cloud notability considerations on Wikipedia

While research has explored the extent of gender bias and the barriers to women's inclusion on English-language Wikipedia, very little research has focused on the problem of racial bias within the encyclopedia. Despite advocacy groups' efforts to incrementally improve representation on Wikipedia, much is unknown regarding how biographies are assessed after creation. Applying a combination of web-scraping, deep learning, natural language processing, and qualitative analysis to pages of academics nominated for deletion on Wikipedia, we demonstrate how Wikipedia's notability guidelines are unequally applied across race and gender. We find that online presence predicts whether a Wikipedia page is kept or deleted for white male academics but that this metric is idiosyncratically applied for female and BIPOC academics. Further, women's pages, regardless of race, were more likely to be deemed “too soon” for Wikipedia. A deeper analysis of the deletion archives reveals that when the tag is used on a woman's biography it is done so outside of the community guidelines, referring to one's career stage rather than media/online coverage. We argue that awareness of hidden biases on Wikipedia is critical to the objective and equitable application of the notability criteria across race and gender both on the encyclopedia and beyond.

How to write a Wikipedia article

How to write an article on Wikipedia is a question that is relevant for many people who contribute to this reference web portal. The creators of Wikipedia have provided various sources from which you can find answers to the main questions. The authors of this study aimed to identify and analyze the main recommendations offered to users who write or edit Wikipedia articles. The results of the study suggest that the basic rules for writing and editing articles include the following: using information from published materials and avoiding personal ideas, opinions, considerations, reflections, dubious or unproven facts; using published materials from reliable independent sources that can be verified; presenting facts, interpretations, and theories without editorial sympathies or bias in order to achieve neutrality (a neutral point of view); and providing complete information covering all aspects that must be described proportionally according to their importance. The authors conclude that these rules are logical and reasonable and are best suited for amateurs who, without adequate knowledge and experience, aim to write acceptable reference content.

Information Acquisition Costs and Post-Earnings Announcement Drift: Evidence from Wikipedia Articles

Information Acquisition Costs and Post-Earnings Announcement Drift: Evidence from Wikipedia Articles

Reassessing the Narrative: A Critique of Wikipedia's Entry on the Franco-Turkish Dispute Over Sanjak of Alexandretta

Reassessing the Narrative: A Critique of Wikipedia's Entry on the Franco-Turkish Dispute Over Sanjak of Alexandretta

Engaging neglected histories: First-year students, archives, and Wikipedia

At the University of California (UC)-Merced, the General Education program recently introduced Spark seminars, a first-year experience that invites incoming undergraduate students into the knowledge-making activity of a research university with a goal of fostering students’ intellectual curiosity, feelings of inclusion, and connections to place. To cultivate these dispositions, faculty are encouraged to create contexts for student research using campus and/or community resources. In spring 2020, Catherine Koehler, continuing lecturer in the Merritt Writing Program, approached University Archivist Jerrold Shiroma about the possibility of a pair of instructional sessions that would introduce students to archives and archival research through the UC-Merced Library Special Collections, in particular the library’s digital collection of newsletters authored by Japanese Americans incarcerated in temporary detention centers in the Central Valley during World War II. Koehler also wanted students to communicate their research to audiences beyond the classroom, which involved further collaboration with Shiroma to introduce students to Omeka, produce a digital exhibit, and, recently, partnerer with librarian Sara Davidson Squibb to develop a new Wikipedia page for the Merced Assembly Center. This article provides a description of these collaborations and outlines challenges, student learning, and future directions to integrate archival collections and library instructional projects with undergraduate teaching.

A Place Recommendation Approach Using Word Embeddings in Conceptual Spaces

The way that computing systems digest geographic space is fundamentally different from people’s understanding of space. In human discourse, a geographic space is referred to by a place name, and the reasoning about a place are based on its characteristics. This is in contrast with computing systems where geographical spaces are handled by the definition of coordinate systems. Hence, when recommending places, a recommendation method that leverages textual content, as a medium of communication among people, can be better understood. In this paper, we use elements of Natural Language Processing (NLP), such as Positive Point-wise Mutual Information (PPMI), Term Frequency - Inverse Document Frequency (TF-IDF), and Multi-Dimensional Scaling (MDS), to infer a conceptual space of the items of a place-based recommender system. By applying a Support Vector Machine (SVM) classifier on the resulting conceptual space, some meaningful directions are extracted. Shannon entropy is used as a measure to identify the directions that imply a valid geographic region. We apply the method on a dataset of advertisement descriptions of rental properties and a dataset of Persian Wikipedia articles. The results showed the proposed method is able to measure the similarity of items in the inferred conceptual space with 88% of accuracy. A comparison with BERT algorithm demonstrates the superiority of the proposed method over the baseline models.