Miriam Redi’s research while affiliated with Grenoble Alpes University and other places

Wikipedia is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia was not conceived as a source of original information, but as a gateway to secondary sources: according to Wikipedia’s guidelines, facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the heart of Wikipedia, little is known about how users interact with them. To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first analysis of readers’ interactions with citations. We find that overall engagement with citations is low: about one in 300 page views results in a reference click (0,29% overall; 0,56% on desktop; 0,13% on mobile). Matched observational studies of the factors associated with reference clicking reveal that clicks occur more frequently on shorter pages and on pages of lower quality, suggesting that references are consulted more commonly when Wikipedia itself does not contain the information sought by the user. Moreover, we observe that recent content, open access sources, and references about life events (births, deaths, marriages, etc.) are particularly popular. Taken together, our findings deepen our understanding of Wikipedia’s role in a global information economy where reliability is ever less certain, and source attribution ever more vital.

Википедия является одним из самых посещаемых сайтов в интернете и распространённым источником информации для многих пользователей. В качестве энциклопедии Википедия задумывалась не как источник оригинальной (окончательной) научной информации, а, скорее, как ворота к более глубоким и точным источникам. В соответствии с базовыми принципами Википедии факты должны быть подкреплены надёжными источниками, которые отражают полный спектр всех мнений по данной теме. Хотя цитаты лежат в основе функционирования Википедии, пока мало что известно о том, как пользователи работают с ними. Чтобы закрыть этот пробел, мы создали клиентские (пользовательские) инструменты для ведения записей (журналов) всех взаимодействий со ссылками, идущими из англоязычных статей Википедии на цитируемые ссылки в течение одного месяца, и провели первый анализ взаимодействия читателей с цитатами. Результаты показывают, что в целом вовлечённость в цитаты низкая. Около 300 просмотров страниц приводят к входу на одну ссылку – это составляет всего 0,29%, в том числе 0,56% при работе с настольным компьютером (на рабочем столе) и 0,13% при работе на мобильных устройствах. Сопоставление факторов, связанных с переходами по ссылке, показывает, что переходы происходят чаще на более коротких страницах и на страницах относительно низкого качества. Исходя из этого можно предположить, что ссылки чаще всего требуются, когда Википедия не содержит информацию, которую ищет пользователь. Кроме того, мы обратили внимание, что источники открытого доступа и ссылки о жизненных событиях (рождения, смерти, браки и т.д.) особенно популярны. Собранные воедино, наши выводы углубляют понимание роли Википедии в глобальной информационной экономике, где надёжность становится всё менее определённой, а значение источников становится всё более важным. Справочный формат ACM для ссылок: Тициано Пиккарди, Мириам Реди, Джованни Колавицца и Роберт Вест. 2020. Количественная оценка взаимодействия с цитатами в Википедии. В трудах: Веб-конференция 2020 (WWW’20), 20–24 апр. 2020 г., Тайбэй, Тай-вань. ACM, Нью-Йорк, штат Нью-Йорк, США. 12 стр. https://doi.org/10.1145/3366423.3380300.

Wikipedia, the free online encyclopedia that anyone can edit, is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia is not a source of original information, but was conceived as a gateway to secondary sources: according to Wikipedia's guidelines, facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the very heart of Wikipedia, little is known about how users interact with them. To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first analysis of readers' interaction with citations on Wikipedia. We find that overall engagement with citations is low: about one in 300 page views results in a reference click (0.29% overall; 0.56% on desktop; 0.13% on mobile). Matched observational studies of the factors associated with reference clicking reveal that clicks occur more frequently on shorter pages and on pages of lower quality, suggesting that references are consulted more commonly when Wikipedia itself does not contain the information sought by the user. Moreover, we observe that recent content, open access sources and references about life events (births, deaths, marriages, etc) are particularly popular. Taken together, our findings open the door to a deeper understanding of Wikipedia's role in a global information economy where reliability is ever less certain, and source attribution ever more vital.

Multimodal learning, which is simultaneous learning from different data sources such as audio, text, images; is a rapidly emerging field of Machine Learning. It is also considered to be learning on the next level of abstraction, which will allow us to tackle more complicated problems such as creating cartoons from a plot or speech recognition based on lips movement. In this paper, we will introduce a basic model to recommend the most relevant images for a Wikipedia article based on state-of-the-art multimodal techniques. We will also introduce the Wikipedia multimodal dataset, containing more than 36,000 high-quality articles

Multimodal learning, which is simultaneous learning from different data sources such as audio, text, images; is a rapidly emerging field of Machine Learning. It is also considered to be learning on the next level of abstraction, which will allow us to tackle more complicated problems such as creating cartoons from a plot or speech recognition based on lips movement. In this paper, we propose to research whether state-of-the-art techniques of multimodal learning, will solve the problem of recommending the most relevant images for a Wikipedia article. In other words, we need to create a shared text-image representation of an abstract notion which paper describes, so that having only a text description machine would ”understand” which images would visualize the same notion accurately.

Wikipedia is playing an increasingly central role on the web, and the policies its contributors follow when sourcing and fact-checking content affect million of readers. Among these core guiding principles, verifiability policies have a particularly important role. Verifiability requires that information included in a Wikipedia article be corroborated against reliable secondary sources. Because of the manual labor needed to curate Wikipedia at scale, however, its contents do not always evenly comply with these policies. Citations (i.e. reference to external sources) may not conform to verifiability requirements or may be missing altogether, potentially weakening the reliability of specific topic areas of the free encyclopedia. In this paper, we aim to provide an empirical characterization of the reasons why and how Wikipedia cites external sources to comply with its own verifiability guidelines. First, we construct a taxonomy of reasons why inline citations are required, by collecting labeled data from editors of multiple Wikipedia language editions. We then crowdsource a large-scale dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design algorithmic models to determine if a statement requires a citation, and to predict the citation reason . We evaluate the accuracy of such models across different classes of Wikipedia articles of varying quality, and on external datasets of claims annotated for fact-checking purposes.

Wikipedia is playing an increasingly central role on the web,and the policies its contributors follow when sourcing and fact-checking content affect million of readers. Among these core guiding principles, verifiability policies have a particularly important role. Verifiability requires that information included in a Wikipedia article be corroborated against reliable secondary sources. Because of the manual labor needed to curate and fact-check Wikipedia at scale, however, its contents do not always evenly comply with these policies. Citations (i.e. reference to external sources) may not conform to verifiability requirements or may be missing altogether, potentially weakening the reliability of specific topic areas of the free encyclopedia. In this paper, we aim to provide an empirical characterization of the reasons why and how Wikipedia cites external sources to comply with its own verifiability guidelines. First, we construct a taxonomy of reasons why inline citations are required by collecting labeled data from editors of multiple Wikipedia language editions. We then collect a large-scale crowdsourced dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design and evaluate algorithmic models to determine if a statement requires a citation, and to predict the citation reason based on our taxonomy. We evaluate the robustness of such models across different classes of Wikipedia articles of varying quality, as well as on an additional dataset of claims annotated for fact-checking purposes.

This quarter, our Community column is dedicated to the review process of ACM Multimedia (MM). We report the summary of discussions arisen at various points in time, after the first round of reviews were returned to authors.