No Arabic abstract
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.
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.
On social media platforms, like Twitter, users are often interested in gaining more influence and popularity by growing their set of followers, aka their audience. Several studies have described the properties of users on Twitter based on static snapshots of their follower network. Other studies have analyzed the general process of link formation. Here, rather than investigating the dynamics of this process itself, we study how the characteristics of the audience and follower links change as the audience of a user grows in size on the road to users popularity. To begin with, we find that the early followers tend to be more elite users than the late followers, i.e., they are more likely to have verified and expert accounts. Moreover, the early followers are significantly more similar to the person that they follow than the late followers. Namely, they are more likely to share time zone, language, and topics of interests with the followed user. To some extent, these phenomena are related with the growth of Twitter itself, wherein the early followers tend to be the early adopters of Twitter, while the late followers are late adopters. We isolate, however, the effect of the growth of audiences consisting of followers from the growth of Twitters user base itself. Finally, we measure the engagement of such audiences with the content of the followed user, by measuring the probability that an early or late follower becomes a retweeter.
Far-right actors are often purveyors of Islamophobic hate speech online, using social media to spread divisive and prejudiced messages which can stir up intergroup tensions and conflict. Hateful content can inflict harm on targeted victims, create a sense of fear amongst communities and stir up intergroup tensions and conflict. Accordingly, there is a pressing need to better understand at a granular level how Islamophobia manifests online and who produces it. We investigate the dynamics of Islamophobia amongst followers of a prominent UK far right political party on Twitter, the British National Party. Analysing a new data set of five million tweets, collected over a period of one year, using a machine learning classifier and latent Markov modelling, we identify seven types of Islamophobic far right actors, capturing qualitative, quantitative and temporal differences in their behaviour. Notably, we show that a small number of users are responsible for most of the Islamophobia that we observe. We then discuss the policy implications of this typology in the context of social media regulation.
There is evidence of misinformation in the online discourses and discussions about the COVID-19 vaccines. Using a sample of 1.6 million geotagged English tweets and the data from the CDC COVID Data Tracker, we conduct a quantitative study to understand the influence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the U.S. from April 19 when U.S. adults were vaccine eligible to May 7, 2021, after controlling state-level factors such as demographics, education, and the pandemic severity. We identify the tweets related to either misinformation or fact-based news by analyzing the URLs. By analyzing the content of the most frequent tweets of these two groups, we find that their structures are similar, making it difficult for Twitter users to distinguish one from another by reading the text alone. The users who spread both fake news and fact-based news tend to show a negative attitude towards the vaccines. We further conduct the Fama-MacBeth regression with the Newey-West adjustment to examine the effect of fake-news-related and fact-related tweets on the vaccination rate, and find marginally negative correlations.
Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from ~20 million unique Covid-19-related tweets from 12 countries posted between 10th March -- 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. Semantic network analysis based on word co-occurrences reveals changes in the emotional framing of Covid-19 casualties that are consistent with this hypothesis. We also find that the average attention afforded to national Covid-19 mortality rates is modelled accurately with the Weber-Fechner and power law functions of sensory perception. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity by country to the national Covid-19 death rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.