Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userInfluence of fake news in Twitter during the 2016 US presidential election
221
0
0.0
(
0
)
Added by
Alexandre Bovet
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.
rate research
Read More
It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called troll accounts were able to manipulate public opinion is still in question. Here we aim to quantify the influence of troll accounts and the impact they had on Twitter by analyzing 152.5 million tweets from 9.9 million users, including 822 troll accounts. The data collected during the US election campaign, contain original troll tweets before they were deleted by Twitter. From these data, we constructed a very large interaction graph; a directed graph of 9.3 million nodes and 169.9 million edges. Recently, Twitter released datasets on the misinformation campaigns of 8,275 state-sponsored accounts linked to Russia, Iran and Venezuela as part of the investigation on the foreign interference in the 2016 US election. These data serve as ground-truth identifier of troll users in our dataset. Using graph analysis techniques we qualify the diffusion cascades of web and media context that have been shared by the troll accounts. We present strong evidence that authentic users were the source of the viral cascades. Although the trolls were participating in the viral cascades, they did not have a leading role in them and only four troll accounts were truly influential.
It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called troll accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter itself. We construct and analyse a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques, along with a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts and we find that one troll account appears in the top-100 and four in the top-1000. This combined with other findings presented in this paper constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000 respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election.
The advent of social media changed the way we consume content favoring a disintermediated access and production. This scenario has been matter of critical discussion about its impact on society. Magnified in the case of Arab Spring or heavily criticized in the Brexit and 2016 U.S. elections. In this work we explore information consumption on Twitter during the last European electoral campaign by analyzing the interaction patterns of official news sources, fake news sources, politicians, people from the showbiz and many others. We extensively explore interactions among different classes of accounts in the months preceding the last European elections, held between 23rd and 26th of May, 2019. We collected almost 400,000 tweets posted by 863 accounts having different roles in the public society. Through a thorough quantitative analysis we investigate the information flow among them, also exploiting geolocalized information. Accounts show the tendency to confine their interaction within the same class and the debate rarely crosses national borders. Moreover, we do not find any evidence of an organized network of accounts aimed at spreading disinformation. Instead, disinformation outlets are largely ignored by the other actors and hence play a peripheral role in online political discussions.
Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world. Persistent manipulation of social media has resulted in increased concerns regarding the 2020 U.S. Presidential Election, due to its potential to influence individual opinions and social dynamics. In this work, we focus on the identification of distorted facts, in the form of unreliable and conspiratorial narratives in election-related tweets, to characterize discourse manipulation prior to the election. We apply a detection model to separate factual from unreliable (or conspiratorial) claims analyzing a dataset of 242 million election-related tweets. The identified claims are used to investigate targeted topics of disinformation, and conspiracy groups, most notably the far-right QAnon conspiracy group. Further, we characterize account engagements with unreliable and conspiracy tweets, and with the QAnon conspiracy group, by political leaning and tweet types. Finally, using a regression discontinuity design, we investigate whether Twitters actions to curb QAnon activity on the platform were effective, and how QAnon accounts adapt to Twitters restrictions.
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
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
