6 Seconds of Sound and Vision: Creativity in Micro-Videos

The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3,800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.Comment: 8 pages, 1 figures, conference IEEE CVPR 201

Beautiful and damned. Combined effect of content quality and social ties on user engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.Comment: ICWSM 201

On the Value of Wikipedia as a Gateway to the Web

By linking to external websites, Wikipedia can act as a gateway to the Web. To date, however, little is known about the amount of traffic generated by Wikipedia's external links. We fill this gap in a detailed analysis of usage logs gathered from Wikipedia users' client devices. Our analysis proceeds in three steps: First, we quantify the level of engagement with external links, finding that, in one month, English Wikipedia generated 43M clicks to external websites, in roughly even parts via links in infoboxes, cited references, and article bodies. Official links listed in infoboxes have by far the highest click-through rate (CTR), 2.47% on average. In particular, official links associated with articles about businesses, educational institutions, and websites have the highest CTR, whereas official links associated with articles about geographical content, television, and music have the lowest CTR. Second, we investigate patterns of engagement with external links, finding that Wikipedia frequently serves as a stepping stone between search engines and third-party websites, effectively fulfilling information needs that search engines do not meet. Third, we quantify the hypothetical economic value of the clicks received by external websites from English Wikipedia, by estimating that the respective website owners would need to pay a total of $7--13 million per month to obtain the same volume of traffic via sponsored search. Overall, these findings shed light on Wikipedia's role not only as an important source of information, but also as a high-traffic gateway to the broader Web ecosystem.Comment: The Web Conference WWW 2021, 12 page

Wikipedia and Westminster: Quality and Dynamics of Wikipedia Pages about UK Politicians

Wikipedia is a major source of information providing a large variety of content online, trusted by readers from around the world. Readers go to Wikipedia to get reliable information about different subjects, one of the most popular being living people, and especially politicians. While a lot is known about the general usage and information consumption on Wikipedia, less is known about the life-cycle and quality of Wikipedia articles in the context of politics. The aim of this study is to quantify and qualify content production and consumption for articles about politicians, with a specific focus on UK Members of Parliament (MPs). First, we analyze spatio-temporal patterns of readers' and editors' engagement with MPs' Wikipedia pages, finding huge peaks of attention during election times, related to signs of engagement on other social media (e.g. Twitter). Second, we quantify editors' polarisation and find that most editors specialize in a specific party and choose specific news outlets as references. Finally we observe that the average citation quality is pretty high, with statements on 'Early life and career' missing citations most often (18%).Comment: A preprint of accepted publication at the 31ST ACM Conference on Hypertext and Social Media (HT'20

A Comparative Study of Reference Reliability in Multiple Language Editions of Wikipedia

Information presented in Wikipedia articles must be attributable to reliable published sources in the form of references. This study examines over 5 million Wikipedia articles to assess the reliability of references in multiple language editions. We quantify the cross-lingual patterns of the perennial sources list, a collection of reliability labels for web domains identified and collaboratively agreed upon by Wikipedia editors. We discover that some sources (or web domains) deemed untrustworthy in one language (i.e., English) continue to appear in articles in other languages. This trend is especially evident with sources tailored for smaller communities. Furthermore, non-authoritative sources found in the English version of a page tend to persist in other language versions of that page. We finally present a case study on the Chinese, Russian, and Swedish Wikipedias to demonstrate a discrepancy in reference reliability across cultures. Our finding highlights future challenges in coordinating global knowledge on source reliability.Comment: Conference on Information & Knowledge Management (CIKM '23

InnerView: Learning Place Ambiance from Social Media Images

In the recent past, there has been interest in characterizing the physical and social ambiance of urban spaces to understand how people perceive and form impressions of these environments based on physical and psychological constructs. Building on our earlier work on characterizing ambiance of indoor places, we present a methodology to automatically infer impressions of place ambiance, using generic deep learning features extracted from images publicly shared on Foursquare. We base our methodology on a corpus of 45,000 images from 300 popular places in six cities on Foursquare. Our results indicate the feasibility to automatically infer place ambiance with a maximum R-2 of 0.53 using features extracted from a pre-trained convolutional neural network. We found that features extracted from deep learning with convolutional nets consistently outperformed individual and combinations of several low-level image features (including Color, GIST, HOG and LBP) to infer all the studied 13 ambiance dimensions. Our work constitutes a first study to automatically infer ambiance impressions of indoor places from deep features learned from images shared on social media

Количественные характеристики работы с цитатами в Википедии. (Часть 1)

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