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Register a new userTweeting for the Cause: Network analysis of UK petition sharing
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Added by
Taha Yasseri
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
Online government petitions represent a new data-rich mode of political participation. This work examines the thus far understudied dynamics of sharing petitions on social media in order to garner signatures and, ultimately, a government response. Using 20 months of Twitter data comprising over 1 million tweets linking to a petition, we perform analyses of networks constructed of petitions and supporters on Twitter, revealing implicit social dynamics therein. We find that Twitter users do not exclusively share petitions on one issue nor do they share exclusively popular petitions. Among the over 240,000 Twitter users, we find latent support groups, with the most central users primarily being politically active average individuals. Twitter as a platform for sharing government petitions, thus, appears to hold potential to foster the creation of and coordination among a new form of latent support interest groups online.
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Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of algorithmic regulation.
Parler is as an alternative social network promoting itself as a service that allows to speak freely and express yourself openly, without fear of being deplatformed for your views. Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.
We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as tool to implement a Turing-like test for go simulators.
Today, an estimated 75% of the British public access information about politics and public life online, and 40% do so via social media. With this context in mind, we investigate information sharing patterns over social media in the lead-up to the 2019 UK General Elections, and ask: (1) What type of political news and information were social media users sharing on Twitter ahead of the vote? (2) How much of it is extremist, sensationalist, or conspiratorial junk news? (3) How much public engagement did these sites get on Facebook in the weeks leading and (4) What are the most common narratives and themes relayed by junk news outlets
We present a benchmark database of public social media postings from the United Kingdom related to the Covid-19 pandemic for academic research purposes, along with some initial analysis, including a taxonomy of key themes organised by keyword. This release supports the findings of a research study funded by the Scottish Government Chief Scientist Office that aims to investigate social sentiment in order to understand the response to public health measures implemented during the pandemic.
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