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Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak

As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it are also growing. Here we estimate the prevalence of links to low-credibility information on Twitter during the outbreak, and the role of bots in spreading these links. We find that the the combined volume of tweets linking to various low-credibility information is comparable to the volume of New York Times articles and CDC links. Content analysis reveals a politicization of the pandemic. The majority of this content spreads via retweets. Social bots are involved in both posting and amplifying low-credibility information, although the majority of volume is generated by likely humans. Some of these accounts appear to amplify low-credibility sources in a coordinated fashion.


Introduction
As we write this paper, most countries across the world are experiencing an unprecedented outbreak of the novel coronavirus . Millions of people have tested positive for the virus and tens of thousands people have died from it globally (coronavirus.jhu.edu/map.html). At the same time, we have observed an increase of approximately 25% in Twitter volume.
With millions of people stuck in their homes and accessing information via social media, concerns about the spread of misinformation about the pandemic (referred to as "infodemic" (Zarocostas 2020)) have mounted. Social media have been known to facilitate the spread of misinformation (Vosoughi, Roy, and Aral 2018), manipulation (Stella, Ferrara, and De Domenico 2018), and radicalization of users (Thompson 2011). These issues are even more pressing in the current atmosphere since the information flowing through social media is directly related to the health and safety of the people.
In response, quite a few research papers have been made public lately that estimate the prevalence of COVID19related misinformation on social media (Cinelli et al. 2020;Pulido et al. 2020;Laato et al. 2020) and characterize the Copyright c 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. behaviors of inauthentic actors (Gallotti et al. 2020;Ferrara 2020). These studies use different methods on different datasets and yield different perspectives on the issue. However, given the complex nature of the problem, many questions remain unanswered. In this paper, we use a random sample of tweets to estimate the prevalence of COVID19related low-credibility information on Twitter and further characterize the role of social bots Shao et al. 2018).

Identification of low-credibility information
Identification of false information often requires factchecking from experts, which is extremely time consuming and therefore not viable for this analysis. Instead, we focus on the URLs embedded in the tweets and annotate the credibility of the content not at the URL level but at the domain level following the literature (Shao et al. 2018;Grinberg et al. 2019;Guess, Nagler, and Tucker 2019;Pennycook and Rand 2019;Bovet and Makse 2019;Vosoughi, Roy, and Aral 2018).
We compiled a list of low-credibility (including hyperpartisan) sources from several lists used in recent research. Our list includes sources that fulfill any one of the following criteria: (1) labeled as low-credibility by Shao et al. (Shao et al. 2018); (2) labeled as "Black" or "Red" or "Satire" by Grinberg et al. (Grinberg et al. 2019); (3) labeled as "fakenews" or "hyperpartisan" by Pennycook et al. (Pennycook and Rand 2019); or (4) labeled as "extremeleft" or "extremeright" or "fakenews" by Bovet et al. (Bovet and Makse 2019). This gives us a list of 570 low-credibility sources.

Data collection
We collected two datasets using different methods to answer different questions.
DS1 consists of tweets containing a set of hashtags and links. Various hashtags are associated to the coronavirus (Chen, Lerman, and Ferrara 2020), but some are focused on certain aspects of the outbreak and some reflect certain biases. To provide a general and unbiased view of the discussion, we chose two generic hashtags #coronavirus and #covid19 as our seeds. Our data was collected using an API from the Observatory on Social Media, which allows to search tweets from a 10% random sample of public tweets ). This dataset consists of tweets from Mar. 9-29, 2020.
Estimating the prevalence of low-credibility information requires matching the URL domains from the tweets against the list defined above. To include all links from the tweet objects obtained through the API, we used a regular expression to extract any URL-like strings from the tweet text in addition to fetching URLs from the entity metadata. For retweets, we also included the URLs in the original tweets using the same method.
Since shortened URLs are very common, we identified those from 70 most frequent shortening services and expanded the URLs through HTTP requests to obtain the real domains. The expanded URLs were further cleaned. We removed those linking to Twitter itself, which turn out to be the majority, and those linking to other social media sites. While these links might still lead to low-credibility information, there is no easy way to verify, so we exclude them in this study. The remaining links mainly belong to news outlets and authorities like government agencies. The DS1 is the set of tweets that match the COVID-19 hashtags and containing any of these links.
DS2 starts from a collection of tweets containing links to low-credibility sources. The data was collected using the Twitter streaming/filter API from Feb. 1 to Apr. 27, 2020. The URLs were extracted and the corresponding web pages were fetched. To reveal common low-credibility information topics, we analyzed the titles of the linked articles and retained those with keywords "coronavirus" and "covid." We ranked the links by the number of tweets containing them and extracted the top 1,200. Each URL in DS2 has been shared at least 50 times.

Bot detection
Social bots are social media accounts controlled in part by algorithms . Malicious bots are known to spread low-credibility information (Shao et al. 2018) as well as creating confusion in the online debate about healthrelated topics like vaccination (Broniatowski et al. 2018). It is therefore interesting to characterize the role of social bots in spreading COVID19-related low-credibility information.
We adopt BotometerLite (Yang et al. 2019), a bot detection model that enables large-scale bot detection. By strategically selecting a subset of the training dataset, Botome-terLite achieves high accuracy in cross-validation as well as cross-domain tests. BotometerLite generates a score between 0 and 1 for every account, with higher scores indicating bot-like profiles. For binary classification of accounts we use a threshold of 0.5 in this paper.

Prevalence of low-credibility information
To report on the prevalence of low-credibility information, we obtain reference volume levels using links to the New York Times, a mainstream news source, and the CDC, an official source of critical information related to the outbreak.  Percentage of (c) retweets and (d) tweets by likely bots for low-credibility, nytimes, cdc.gov, and overall links in DS1.
Our results show that links to low-credibility sources combined contribute 0.89% of the total tweet volume in DS1 ( Fig. 1(a)). For comparison, nytimes.com contributes 0.98% and cdc.gov contributes less than 0.65%. To account for the fact that some users might share certain information repeatedly, we provide the same analysis at the level of users, i.e., the percentage of users who shared the corresponding links at least once, in Fig. 1(b). The results are qualitatively similar. These findings suggest that low-credibility information is not rampant on Twitter, but it does have a volume share comparable with highly reliable sources. We also show the percentage of retweets for different sources in Fig. 1(c). About 68% of the links to lowcredibility information are shared by retweets. For comparison, this fraction is about 54% for nytimes.com and all URLs together in DS1. This suggests that users involved with lowcredibility information on Twitter are more likely to share links posted by others. Interestingly, cdc.gov has an even hither retweet rate.

Role of social bots
We report the percentage of tweets posted by social bots for different sources in Fig. 1(d). A significantly higher ratio of the volume of low-credibility information is shared by likely bot accounts, compared to the volume of tweets linking to reliable sources and the overall baseline. Since some accounts post multiple tweets with the same link, affecting the bot ratio estimation, we also perform the same analysis at the user level (not shown in Fig. 1). The bot ratios become 12.1%, 6.5%, 10.6%, and 11.7% for low-credibility,  nytimes.com, cdc.gov, and overall links, respectively. The decreases in bot volume ratio compared to the tweet-level analysis suggest that low-credibility links are amplified by hyper-active bots. These results are consistent with previous findings (Shao et al. 2018). Let us focus on retweets to characterize the interaction between original posters of links and accounts that amplify the reach of those links. The results for different sources are shown in Fig. 2. For low-credibility information, although the majority of tweets and retweets are posted by humanlike accounts, we do see a higher-than-normal participation rate of bot accounts in both posting and amplifying the content. We also find that bot-like tweeters attract more bot-like retweeters than human-like tweeters (see highlighted oval in Fig. 2(a)), despite a majority of human-like retweeters.
We see in Fig. 2(b) that bot participation is much lower for nytimes.com than low-credibility sources. The pattern for cdc.gov is interesting (Fig. 2(c)). Original posting is dominated by the official account @CDCgov, whereas the retweeters have a much broader score spectrum with a relatively high proportion of bot-like accounts. This suggests that some bots are also trying to disseminate useful information, in agreement with previous findings (Ferrara 2020).

Coordinated amplification of low-credibility information
Let us build a network of shared low-credibility domains to highlight potentially coordinated groups of accounts amplifying misinformation (Pacheco et al. 2020). We focus on accounts that share at least 3 links. Then we extract domains from shared links to low-credibility sources, and represent an account as a vector of such domains. We finally calculate the cosine similarity between each pair of account vectors and use it as a network weight.
The resulting network is shown in Fig. 3. We note a few densely connected clusters of accounts, which share links to many of the same low-credibility sources. Although this network is not dominated by accounts with high bot scores, manual inspection reveals that several of the accounts generate suspiciously high volumes of partisan content.

Topics from low-credibility sources
We wish to provide a sense of the common topics of linked articles from low-credibility sources. Fig. 4 depicts the most frequent words in the titles of the articles in DS2, excluding the query terms "coronavirus" and "covid." Popular topics covered by low-credibility sources are U.S. politics, the status of the outbreak, and economic issues.
This analysis suggests a politicization of the pandemic. An example revolves around claims that the COVID-19 pandemic originated from a weaponized virus. One of the most popular sources pushing this narratives is ZeroHedge.com, the third most-shared low-credibility domain in our dataset. Notably, this occurs despite the fact that Twitter suspended the ZeroHedge account for violating the platform's manipulation policy at the beginning of the pandemic (bloomberg.com/news/articles/2020-02-01/zero-hedgepermanently-suspended-from-twitter-for-harassment).

Discussion
We characterize the prevalence of low-credibility information on Twitter during the novel coronavirus outbreak. The combined prevalence of various low-credibility sources is comparable with mainstream and reliable sources. Consistent with previous research, social bots are more likely to get involved in posting and amplifying low-credibility information. Finally, we find evidence of coordinated activity amplifying low-credibility content.
The analyses presented here are preliminary and have several limitations. First, the sampling method based on two hashtags might introduce unknown biases. Second, the domain-based identification of low-credibility sources is not exhaustive and cannot capture misinformation contained in the content of tweets like text, images, and videos. Third, it is impossible to draw any conclusion about impact from Figure 3: Similarity network of accounts sharing links to low-credibility sources. Nodes are colored using a humanlike (blue) to bot-like (red) scheme and size is proportional to strength (weighted degree). Edge weights represent cosine similarity among link vectors (see text). Only links with weight above 0.8 are shown, and singleton nodes after this filtering are removed. The final network consists of 180 nodes and 1,343 edges. the prevalence of misinformation alone. Fourth, bot detection algorithms are never perfectly accurate and may have biases stemming from training data. And fifth, our coordination analysis may be distorted by popular sources. More thorough analyses are needed to confirm as well as expand our findings.