11 research outputs found
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
Characterising User Content on a Multi-lingual Social Network
Social media has been on the vanguard of political information diffusion in
the 21st century. Most studies that look into disinformation, political
influence and fake-news focus on mainstream social media platforms. This has
inevitably made English an important factor in our current understanding of
political activity on social media. As a result, there has only been a limited
number of studies into a large portion of the world, including the largest,
multilingual and multi-cultural democracy: India. In this paper we present our
characterisation of a multilingual social network in India called ShareChat. We
collect an exhaustive dataset across 72 weeks before and during the Indian
general elections of 2019, across 14 languages. We investigate the cross
lingual dynamics by clustering visually similar images together, and exploring
how they move across language barriers. We find that Telugu, Malayalam, Tamil
and Kannada languages tend to be dominant in soliciting political images (often
referred to as memes), and posts from Hindi have the largest cross-lingual
diffusion across ShareChat (as well as images containing text in English). In
the case of images containing text that cross language barriers, we see that
language translation is used to widen the accessibility. That said, we find
cases where the same image is associated with very different text (and
therefore meanings). This initial characterisation paves the way for more
advanced pipelines to understand the dynamics of fake and political content in
a multi-lingual and non-textual setting.Comment: Accepted at ICWSM 2020, please cite the ICWSM versio
Under the Spotlight: Web Tracking in Indian Partisan News Websites
India is experiencing intense political partisanship and sectarian divisions.
The paper performs, to the best of our knowledge, the first comprehensive
analysis on the Indian online news media with respect to tracking and
partisanship. We build a dataset of 103 online, mostly mainstream news
websites. With the help of two experts, alongside data from the Media Ownership
Monitor of the Reporters without Borders, we label these websites according to
their partisanship (Left, Right, or Centre). We study and compare user tracking
on these sites with different metrics: numbers of cookies, cookie
synchronizations, device fingerprinting, and invisible pixel-based tracking. We
find that Left and Centre websites serve more cookies than Right-leaning
websites. However, through cookie synchronization, more user IDs are
synchronized in Left websites than Right or Centre. Canvas fingerprinting is
used similarly by Left and Right, and less by Centre. Invisible pixel-based
tracking is 50% more intense in Centre-leaning websites than Right, and 25%
more than Left. Desktop versions of news websites deliver more cookies than
their mobile counterparts. A handful of third-parties are tracking users in
most websites in this study. This paper, by demonstrating intense web tracking,
has implications for research on overall privacy of users visiting partisan
news websites in India
Tweeting MPs:Digital Engagement between Citizens and Members of Parliament in the UK
Disengagement and disenchantment with the Parliamentary process is an
important concern in today's Western democracies. Members of Parliament (MPs)
in the UK are therefore seeking new ways to engage with citizens, including
being on digital platforms such as Twitter. In recent years, nearly all (579
out of 650) MPs have created Twitter accounts, and have amassed huge followings
comparable to a sizable fraction of the country's population. This paper seeks
to shed light on this phenomenon by examining the volume and nature of the
interaction between MPs and citizens. We find that although there is an
information overload on MPs, attention on individual MPs is focused during
small time windows when something topical may be happening relating to them.
MPs manage their interaction strategically, replying selectively to UK-based
citizens and thereby serving in their role as elected representatives, and
using retweets to spread their party's message. Most promisingly, we find that
Twitter opens up new avenues with substantial volumes of cross-party
interaction, between MPs of one party and citizens who support (follow) MPs of
other parties.Comment: A preprint of accepted publication at the International AAAI
Conference On Web And Social Media (ICWSM) 201
Visual Gender Biases in Wikipedia: A Systematic Evaluation across the Ten Most Spoken Languages
Wikipedia, a collaborative crowd-sourced platform, is the largest, most visited online encyclopedia. Since it spreads information freely in more than 300 languages, many users, tools, and dashboards rely on its content. Hence, there is a need to maintain its fairness and completeness. However, previous research has indicated the existence of a significant gender gap in Wikipedia biographical articles. We already know that a minimal proportion of those articles portray women and there are gender asymmetries in the textual content of these articles, but little has been reported about the visual aspects (e.g., image volume or image quality) of the gender gap. Here, we analyze all biographies available on Wikipedia across 300 occupations in the ten most widely spoken languages, and undertake quantitative and qualitative analysis of gender differences in the written and visual content. The cross-lingual results indicate that (1) much of the male bias in content arises when editors select which personalities should have a Wikipedia page, (2) the trends in written and visual content are quite dissimilar, (3) men biographies tend to have more images across languages, and (4) female biographies average better visual quality. A more granular analysis is performed on English Wikipedia, distinguishing trends in occupations and qualitatively analyzing science and technology biographies. The overall results shed light on the kinds of visual biases that emerge in the collaborative creation of Wikipedia and yield guidelines for future management of contributions on the platform