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The COVID-19 social media infodemic

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We analyze engagement and interest in the COVID topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns.

As foreseen in by the global risk report of the World Economic forum, global risks are interconnected. In particular, the case of the COVID epidemic the infectious disease caused by the most recently discovered human coronavirus is showing the critical role of information diffusion in a disintermediated news cycle 2. The term infodemic 3 , 4 has been coined to outline the perils of misinformation phenomena during the management of disease outbreaks 5 , 6 , 7 , since it could even speed up the epidemic process by influencing and fragmenting social response 8.

As an example, CNN has recently anticipated a rumor about the possible lock-down of Lombardy a region in northern Italy to prevent pandemics 9 , publishing the news hours before the official communication from the Italian Prime Minister.

As a result, people overcrowded trains and airports to escape from Lombardy toward the southern regions before the lock-down was put in place, disrupting the government initiative aimed to contain the epidemics and potentially increasing contagion. Thus, an important research challenge is to determine how people seek or avoid information and how those decisions affect their behavior 10 , particularly when the news cycle—dominated by the disintermediated diffusion of information—alters the way information is consumed and reported on.

To this respect, models to forecast virus spreading are starting to account for the behavioral response of the population with respect to public health interventions and the communication dynamics behind content consumption 8 , 11 , Social media platforms such as YouTube and Twitter provide direct access to an unprecedented amount of content and may amplify rumors and questionable information. This shift from the traditional news paradigm profoundly impacts the construction of social perceptions 14 and the framing of narratives; it influences policy-making, political communication, as well as the evolution of public debate 15 , 16 , especially when issues are controversial Users online tend to acquire information adhering to their worldviews 18 , 19 , to ignore dissenting information 20 , 21 and to form polarized groups around shared narratives 22 , Furthermore, when polarization is high, misinformation might easily proliferate 24 , Some studies pointed out that fake news and inaccurate information may spread faster and wider than fact-based news However, this might be platform-specific effect.

Studying the effect of the social media environment on the perception of polarizing topics is being addressed also in the case of COVID The issues related to the current infodemics are indeed being tackled by the scientific literature from multiple perspectives including the dynamics of hatespeech and conspiracy theories 28 , 29 , the effect of bots and automated accounts 30 , and the threats of misinformation in terms of diffusion and opinions formation 31 , In this work we provide an in-depth analysis of the social dynamics in a time window where narratives and moods in social media related to the COVID have emerged and spread.

While most of the studies on misinformation diffusion focus on a single platform 17 , 26 , 33 , the dynamics behind information consumption might be particular to the environment in which they spread on. The dataset includes more than 8 million comments and posts over a time span of 45 days.

We analyze user engagement and interest about the COVID topic, providing an assessment of the discourse evolution over time on a global scale for each platform. These groups are either associated to the diffusion of mostly reliable or mostly questionable contents and we characterize the spreading of information regarding COVID relying on this classification.

We find that users in mainstream platforms are less susceptible to the diffusion of information from questionable sources and that information deriving from news outlets marked either as reliable or questionable do not present significant difference in the way it spreads. Our findings suggest that the interaction patterns of each social media combined with the peculiarity of the audience of each platform play a pivotal role in information and misinformation spreading. We analyze mainstream platforms such as Twitter, Instagram and YouTube as well as less regulated social media platforms such as Gab and Reddit.

Gab is a crowdfunded social media whose structure and features are Twitter-inspired. It performs very little control on content posted; in the political spectrum, its user base is considered to be far-right. Reddit is an American social news aggregation, web content rating, and discussion website based on collective filtering of information.

We perform a comparative analysis of information spreading dynamics around the same argument in different environments having different interaction settings and audiences. The deriving dataset is then composed of 1,, posts and 7,, comments produced by 3,, users. For more details regarding the data collection refer to Methods. First, we analyze the interactions i.

The upper panel of Fig. This entails that users behave similarly for what concern the dynamics of reactions and content consumption. The highest volume of interactions in terms of posting and commenting can be observed on mainstream platforms such as YouTube and Twitter.

Upper panel: activity likes, comments, reposts, etc. Lower panel: cumulative number of content posts, tweets, videos, etc. Then, to provide an overview of the debate concerning the disease outbreak, we extract and analyze the topics related to the COVID content by means of Natural Language Processing techniques.

We build word embedding for the text corpus of each platform, i. Moreover, by running clustering procedures on these vector representations, we separate groups of words and topics that are perceived as more relevant for the COVID debate. For further details refer to Methods. The results Fig. Debates range from comparisons to other viruses, requests for God blessing, up to racism, while the largest volume of interaction is related to the lock-down of flights.

Finally, to characterize user engagement with the COVID on the five platforms, we compute the cumulative number of new posts each day Fig. The largest increase in the number of posts is on the 21st of January for Gab, the 24th January for Reddit, the 30th January for Twitter, the 31th January for YouTube and the 5th of February for Instagram.

Thus, social media platforms seem to have specific timings for content consumption; such patterns may depend upon the difference in terms of audience and interaction mechanisms both social and algorithmic among platforms.

Efforts to simulate the spreading of information on social media by reproducing real data have mostly applied variants of standard epidemic models 37 , 38 , 39 , Coherently, we analyze the observed monotonic increasing trend in the way new users interact with information related to the COVID by using epidemic models. Unlike previous works, we do not only focus on models that imply specific growth mechanisms, but also on phenomenological models that emphasize the reproducibility of empirical data In our case, we try to model the growth in number of people publishing a post on a subject as an infective process, where people can start publishing after being exposed to the topic.

We model the dynamics both with the phenomenological model of 43 from now on referred to as the EXP model and with the standard SIR Susceptible, Infected, Recovered compartmental model Further details on the modeling approach can be found in Methods. Growth of the number of authors versus time.

Time is expressed in number of days since 1st January day 1. As shown in Fig. This observation may facilitate the prediction task of information spreading during critical events.

Indeed, according to this result we can consider information spreading patterns on each social media to predict social response when implementing crisis management plans.

We conclude our analysis by comparing the diffusion of information from questionable and reliable sources on each platform. In order to clarify the limits of an approach that is based on labelling news outlets rather than single articles, as for instance performed in 33 , 48 , we report the definitions used in this paper for questionable and reliable information sources.

By reliable information sources we mean news outlets that do not show any of the aforementioned characteristics. By interactions we mean the overall reactions, e. Surprisingly, all the posts show a strong linear correlation, i. We observe the same phenomenon also for the engagement with reliable and questionable sources.

Upper panels: plot of the cumulative number of posts referring to questionable sources versus the cumulative number of posts referring to reliable sources. Lower panel: plot of the cumulative number of engagements relatives to questionable sources versus the cumulative number of engagements relative to reliable sources.

In more popular social media, the number of questionable posts represents a small fraction of the reliable ones; same thing happens in Reddit. Further details concerning the regression coefficients are reported in Methods. In particular, we observe that in mainstream social media the number of posts produced by questionable sources represents a small fraction of posts produced by reliable ones; the same thing happens in Reddit. Such results hint the possibility that different platform react differently to information produced by reliable and questionable news outlets.

Therefore, we conclude that the main drivers of information spreading are related to specific peculiarities of each platform and depends upon the group dynamics of individuals engaged with the topic. Such a timeframe is a good benchmark for studying content consumption dynamics around critical events in a times when the accuracy of information is threatened.

We assess user engagement and interest about the COVID topic and characterize the evolution of the discourse over time. Furthermore, we model the spread of information using epidemic models and provide basic growth parameters for each social media platform.

We then analyze the diffusion of questionable information for all channels, finding that Gab is the environment more susceptible to misinformation dissemination. However, information deriving from sources marked either as reliable or questionable do not present significant differences in their its spreading patterns. We believe that the understanding of social dynamics between content consumption and social media platforms is an important research subject, since it may help to design more efficient epidemic models accounting for social behavior and to design more effective and tailored communication strategies in time of crisis.

Different data collection processes have been performed depending on the platform. The Reddit dataset was downloaded from the Pushift. In Gab, although no official guides are available, there is an API service that given a certain keyword, returns a list of users, hashtags and groups related to it. We queried all the keywords we selected based on Google Trends and we downloaded all hashtags linked to them.

We then manually browsed the results and selected a set of hashtags based on their meaning. For each hashtag in our list, we downloaded all the posts and comments linked to it. Then an in depth search was done by crawling the network of videos by searching for more related videos as established by the YouTube algorithm. From the gathered set, we filtered the videos that matched coronavirus, nCov, corona virus, corona-virus, corvid, covid or SARS-CoV in the title or description.

We then collected all the comments received by those videos. For Twitter, we collect tweets related to the topic coronavirus by using both the search and stream endpoint of the Twitter API. The data derived from the search API represent a random sample of the tweets containing the selected keywords up to a maximum rate limit of tweets every 10 minutes.

Since no official API are available for Instagram data, we built our own process to collect public contents related to our keywords. We manually took notes of posts, comments and populated the Instagram Dataset. We consider all the posts in our dataset that contain at least one URL linking to a website outside the related social media platfrom e.

In that category, each news outlet is associated to a label that refers to its reliability as expressed in three labels, namely Conspiracy-Pseudoscience, Pro-Science or Questionable. Noticeably, also the Questionable set include a wide range of political bias, from Extreme Left to Extreme Right. Using such a classification, we assign to each of these outlets a binary label that partially stems from the labelling provided by MBFC. We divide the news outlets into Questionable and Reliable.

Analyzing Social Media Data and Web Networks

A social network is a social structure made up of a set of social actors such as individuals or organizations , sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology , sociology , statistics , and graph theory. Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations". These approaches were mathematically formalized in the s and theories and methods of social networks became pervasive in the social and behavioral sciences by the s. Together with other complex networks , it forms part of the nascent field of network science.

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Social networking is the use of internet-based social media sites to stay connected with friends, family, colleagues, customers, or clients. Social networking can have a social purpose, a business purpose, or both, through sites like Facebook, Twitter, LinkedIn, and Instagram. Social networking has become a significant base for marketers seeking to engage customers. Despite some stiff competition, Facebook remains the largest and most popular social network, with 2. Social networking involves the development and maintenance of personal and business relationships using technology.

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