The DOI matches its Crossref record; 54 of the 61 references it cites were found in a registry or an index; none of the 18 sources its claims cite is checked for support yet.
Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics
Open accessPublished version of a preprint
Abstract
During COVID-19, governments and the public are fighting not only a pandemic but also a co-evolving infodemic—the rapid and far-reaching spread of information of questionable quality. We analysed more than 100 million Twitter messages posted worldwide during the early stages of epidemic spread across countries (from 22 January to 10 March 2020) and classified the reliability of the news being circulated. We developed an Infodemic Risk Index to capture the magnitude of exposure to unreliable news across countries. We found that measurable waves of potentially unreliable information preceded the rise of COVID-19 infections, exposing entire countries to falsehoods that pose a serious threat to public health. As infections started to rise, reliable information quickly became more dominant, and Twitter content shifted towards more credible informational sources. Infodemic early-warning signals provide important cues for misinformation mitigation by means of adequate communication strategies. An analysis of news shared on Twitter estimates the level of infodemic risk associated with COVID-19 across countries. Epidemic spread and infodemic risk co-evolve, with reliable information becoming more dominant as infection rates rise locally.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Analyzing more than 112 million Twitter messages in 64 languages from 21 January to 10 March 2020, the authors find that waves of unreliable and low-quality COVID-19 information often anticipate epidemic waves, while reliable information spreads after outbreaks hit. They introduce an Infodemic Risk Index and report that global infodemic risk tends to decline as epidemics expand, though country patterns vary substantially.
Key findings
- An estimated 40.4% of online messages during the period were due to automated agents (bots), doubling activity relative to estimates from only four years earlier.
- The authors extracted about 20.7 million links (3.3 million unique); about 50% of URLs were fact-checked by screening almost 4,000 expert-curated web domains, yielding a final database of 3,892 entries: 1,434 reliable, 2,264 unreliable, and 194 unknown.
- Infodemic waves of unreliable/low-quality information anticipate epidemic waves; when epidemics hit an area, reliable information is “inoculated” and focus shifts toward certified sources. The global Infodemic Risk Index tends to decrease as epidemics spread, with average risk on days with no confirmed cases at 0.276, a drop at first reported case, a rebound at 3 confirmed cases, then a decrease as cases grow.
- Country profiles differ: South Korea remains low-risk apart from an early spike; Canada is slightly higher; Venezuela is high-risk throughout; Russia shows erratic, pronounced spikes; Italy’s misinformation collapses after outbreak; and in the USA misinformation is mainly driven by verified sources and remains basically constant. The paper also finds G9 countries with remarkable infodemic risk and developing countries with far lower risk levels.
Methods
- Twitter messages were collected via the Filter API (24 Jan–10 Mar 2020) and Search API (21–24 Jan 2020) using hashtags/keywords including coronavirus, ncov, #Wuhan, covid19, covid-19, sarscov2, and covid. The authors estimate 161.2 million tweets were posted until 10 Mar 2020 and collected 112.6 million; more than 60 million geolocated messages containing more than 9 million news items were analyzed.
- Users were classified as human or non-human (bots) using a deep-learning algorithm with >90% accuracy and >95% precision for bots, while keeping verified/unverified distinctions. URLs were extracted and assigned reliability through fact-checking databases and a Harm Score scheme.
- The authors defined exposure, reliability/unreliability, and an Infodemic Risk Index (IRI) ranging from 0 to 1, aggregated daily and by country. Figure 3 considers 178 countries; Supplementary Fig. 5 uses 177 countries excluding China.
Limitations
- Paper-stated: only about 50% of URLs were fact-checked, and unassessable links were discarded; exposure is a lower bound because higher-order transmission pathways and users who read without resharing are not tracked; China was removed as a major outlier; the relationship with biological contagion patterns cannot be checked in some cases due to lack of reliable data; and whether the USA will show a similar readjustment remains to be checked.
- Paper-stated/evident: Twitter API restrictions after 25 Feb 2020 allowed no more than 4.5 million messages per day on average, so an increasing fraction of tweets was missed (161.2 million estimated posted vs. 112.6 million collected); geolocated analysis relies on about 56% of the total observed volume after geocoding profiles.
- Evident: source reliability inherits labels and Harm Scores from external databases and expert classification; the paper also notes that countries with high infodemic risk might be more unreliable in reporting epidemic data, which could alter perceptions and indirectly mislead people.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
38 Own result · 20 Own interpretation · 11 Own method · 10 Not checked yet · 3 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that waves of unreliable and low-quality information anticipate the epidemic ones, exposing entire countries to irrational social behavior and serious threats for public health.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“By using state-of-the-art machine learning techniques to analyze the online behavior of users (see Methods for details), we have discovered an extraordinary activity of automated agents, referred to as social bots 14,15,35,39 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [14] De Domenico, M., Granell, C., Porter, M. A. & Arenas, A. The physics of spreading processes in multilayer networks. Nat. Phys. 12, 901–906 (2016). source
- Not checked yetcites [15] Brockmann, D. & Helbing, D. The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013). source
- Not checked yetcites [35] Watts, D. J. A simple model of global cascades on random networks. Proc. Natl Acad. Sci. USA 99, 5766–5771 (2002). source
- Not checked yetcites [39] Hébert-Dufresne, L., Scarpino, S. V. & Young, J.-G. Macroscopic patterns of interacting contagions are indistinguishable from social reinforcement. Nat. Phys. https://doi.org/10.1038/s41567-020-0791-2 (2020). source
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In the paper
“Contrary to what it could be expected in principle, what this natural experiment reveals is that, on the verge of a threatening global pandemic emergency due to SARS-CoV-2 [23][24][25] , human communication activity is to a significant extent characterized by the intentional production of informational noise and even of misleading or false information 26 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [26] Weekly Epidemiological Record Vol. 95, 16 (WHO, 2020); https://www.who.int/wer/2020/wer9516/en/
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We first compute the average value for days with no confirmed cases, which is 0.276”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Overall, the global level of infodemic risk tends to decrease as the epidemics spread globally”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the evolution of infodemic risk among countries with both high message volume and significant epidemic contagion tends to be very asymmetric”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, also the dynamic profiles of infodemic risk in countries with similar risk levels may be very different.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unverified users, both humans and bots, share a larger fraction of unreliable news than verified users.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“This analysis demonstrates that reliable sources are more represented than unreliable ones”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
boosted- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, profiles identified as bots have a larger number of followers.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 36
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the epidemics hit the same area, reliable information is quickly inoculated, like antibodies, and the system shifts focus towards certified informational sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Specifically, we estimate that 40.4% of online messages during this period were due to such automated agents, doubling the activity with respect to estimates of only four years ago 35 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [35] Watts, D. J. A simple model of global cascades on random networks. Proc. Natl Acad. Sci. USA 99, 5766–5771 (2002). source
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The average values are: 660 for Unverified Humans (circles), 1400 for Unverified Bots (squares, 51k for Verified Humans (stars) and 240k for Verified Bots (diamonds).”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have found a total of 4,988 domains, reduced to 4,417 after removing hard duplicates across databases.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the contagion spreads to significant levels, the infodemic risk further decreases, signalling an increasing focus of the public opinion toward reliable news sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgeddirectSingle case- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All distributions display a fat-tail, but different categories of users have a different outreach.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“About 0.84% of collected posts were geo-tagged by the user, providing highly accurate information about their geographic location.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Canada presents a slightly higher level of infodemic risk, and unlike South Korea, we see that the risk level increases as the epidemics spread, but stays at low levels.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The difference, possibly caused by the correlation of this kind of behaviour with the choice of how customising the field”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the opposite, in a high-risk country such as Venezuela, the infodemics is in full swing throughout the period of observation, and in addition to the expected activity from unverified sources one notices that even verified ones contribute to a large extent to the infodemics.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Fig. 4 shows, however, that the relationship between reduction of infodemic risk and expansion of the epidemics seems to be a rather general trend, as the relationship between number of confirmed cases and infodemic risk is (nonlinearly) negative, confirming the result shown in Fig. 3.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“with major roles played not only by countries such as Iran, but also United States, Germany, the Netherlands, Austria and Norway maintaining their relative levels, and other countries like Italy, South Korea and Japan significantly reducing it with the progression of the epidemics.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“This phase of processing reduced the overall database to unique 3,920 domains.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Type Count RELIABLE 1434 UNRELIABLE 2264 UNKNOWN 194 Total 3892”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the USA, misinformation is mainly driven by verified sources, and it remains basically constant even after the epidemics outbreak.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe that reporting of the first case is associated with a drop in the risk index, followed by an increase back to the”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelationaln = 3- Asserted: passes — The authors put it forward as true.
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In the paper
“We can observe a first increase in collective attention after the outbreak in Wuhan, China (between 24 Jan and 02 Feb 2020) and a second strong rise after the epidemics began to spread in northern Italy (20 Feb 2020 onwards).”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
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In the paper
“In the case of Italy the risk is mostly due to the activity of unverified sources, but we notice that with the outbreak of the epidemics, the production of misinformation literally collapses and there is a sudden shift to reliable sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, we find that there are G9 countries with remarkable infodemic risk and developing countries with far lower risk levels.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Here, we have shown that in the context of the COVID-19 crisis, complex infodemic effects are indeed at work, with significant variations across countries, where level of socio-economic development is not the key discriminant to separate countries with high vs. low infodemic risk.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
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In the paper
“The fraction of Geoolocated (messages with shared locations, or geonamed, indicated in green) is constantly about 56% of the total volume recorded”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
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In the paper
“Finally, in a relatively high-risk country such as Russia we notice that infodemic risk is erratic with sudden, very pronounced spikes, and again also verified sources play a major role.Here too, information about the epidemics is fragmented and mostly unreliable.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This action reduced the database to the final number of 3,892 entries”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“About 50% of URLs have been fact-checked by screening almost 4,000 expert-curated web domains, whereas the remaining corpus was pointing to disappeared web pages or to content not classifiable automatically (eg, videos on YouTube) and unpopular sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The reliability of a URL can be inherited from the reliability of its source domain.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As users are repeatedly hit by a given message from different sources, this works as an indirect validation of its reliability and relevance, leading the user to spread it in turn, and to become an informationally infectious agent.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Contrary to mainstream beliefs, we show that human response to falsehood exhibits early-warning signals that might be mitigated with adequate communication strategies.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Recently, it has been suggested that this challenging phenomenon might be due to the fact that, at population level, the dynamics of multiple interacting contagions are indistinguishable from social reinforcement 38 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [38] Stella, M., Cristoforetti, M. & De Domenico, M. Influence of augmented humans in online interactions during voting events. PLoS ONE 14, e0214210 (2019). source
hedged- Asserted: fails — The authors may not put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“In this study, we show that information dynamics tailored to alter individuals' perceptions and, consequently, their behavioral response, is able to drive collective attention 12 towards false 13,14 or inflammatory 15 content, a phenomenon named infodemics [16][17][18][19] , sharing similarities with more traditional epidemics and spreading phenomena [20][21][22] .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [12] Zarocostas, J. How to fight an infodemic. Lancet 395, 676 (2020). source
- Not checked yetcites [13] Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–979 (2015). source
- Not checked yetcites [14] De Domenico, M., Granell, C., Porter, M. A. & Arenas, A. The physics of spreading processes in multilayer networks. Nat. Phys. 12, 901–906 (2016). source
- Not checked yetcites [15] Brockmann, D. & Helbing, D. The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013). source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Fig. 1, an infodemics is the result of the simultaneous action of multiple human and non-human sources of fake or unreliable news.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In fact, the dangerousness of infodemics can compare and sum up to a large extent to that of the epidemics itself 29 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [29] Ferrara, E., Varol, O., Davis, C., Menczer, F. & Flammini, A. The rise of social bots. Commun. ACM 59, 96–104 (2016). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Perceived and actual biological and social risks feed upon one another, and may co-evolve in complex ways.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Human and non-human accounts forge unreliable content -such as fake or untrustworthy news -about the COVID19, a topic attracting the collective attention of the whole world.Their followers are exposed to such content, and reshare it, becoming infectious agents:”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“More in detail, our method has the highest accuracy (>90%) and precision in identifying bots (>95%) when compared with state-of-the-art methods.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Human vs non-human classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting that evidence of the expansion of the contagion leads people to look for relatively more reliable sources, and that verified influencers with many followers started inoculating the system with more reliable news (see Supplementary Figures 3 and 4), playing a role that presents interesting analogies to that of antibodies in the treatment of an infectious disease.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Especially in situations where effective therapies to contrast the diffusion of the pandemic are not readily available, coordination of behaviors and diffusion of pro-social orientations driven by reliable information at all scales are the key resources for the mitigation of adverse effects.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We estimate that until 24 Feb 2020 we lost about 60,000 tweets out of millions, capturing more than 99.5% of all messages posted (see Supplementary Fig. 1).”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is also interesting to observe though that countries with high infodemic risk might also be more unreliable in terms of reporting of epidemic data, thus altering the perceptions of people and indirectly misleading them in their search for reliable information.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The escalation of the epidemics leads people to progressively pay attention to more reliable sources thus potentially limiting the impact of the infodemics, but the actual speed of adjustment may make a major difference in determining the social outcome, and in particular between a controlled epidemics and a global pandemics.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“IRI is robust to user classification, making it an indicator not sensitive to performances of bot detection algorithms.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We have collected manually-checked web domains from multiple publicly available databases, including scientific and journalistic ones.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Data availability
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Note that this choice is the safest as to date: in fact, it has been recently shown that biases affecting Sample API (which samples data based on rate limits), for instance, are not found in REST and Filter APIs 40 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [40] Eysenbach, G. How to fight an infodemic: the four pillars of infodemic management. J. Med. Internet Res. 22, e21820 (2020). source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This casts new light on the social mechanics of the infodemics-epidemics interaction, and may be of help to policy makers to design a more integrated strategic approach, by suitably embedding communication and information management into a comprehensive, extended public health perspective.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We have estimated a total of 161.2 millions tweets posted until 10 Mar 2020: we have successfully collected 112.6 millions of them, providing an unprecedented opportunity for infodemics analysis.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“however, they circulate in different ways and reach different targets, a feature that is perfectly captured by the infodemic risk index introduced in this study.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The effect is particularly pronounced with the escalation of the epidemics, suggesting that this effect could be mediated by levels of perceived social alarm.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“When two or more domains are soft duplicates, we keep the classification with the highest Harm Score, as a conservative choice.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
Verified users are more authentic than unverified users because Twitter applies strict verification criteria.
“In fact, verified users should be considered as more authentic than unverified ones, because Twitter makes use of strict criteria for verification.”
The paper relies on this without support.
Twitter messages are a valid proxy for global human communication activity during the pandemic.
“We focus our attention on the analysis of messages posted on a popular microblogging platform 30 , an online social network characterized by heterogeneous connectivity 31 and topological shortcuts typical of small-world systems 32 .”
The paper relies on this without support.
The reliability of a URL can be inherited from the reliability of its source domain.
“Each URL is therefore used for fact-checking, inheriting the reliability of its source (see Methods).”
The paper relies on this without support.
The machine learning classifier used to distinguish human from bot accounts is accurate.
“we have used an accurate machine learning approach to classify the author as human or non-human (i.e., bot)”
The authors state this as an assumption.
Exposure can be approximated by the number of followers of the posting user at first order.
“The number of followers K u of a single user u defines the exposure, in terms of potential visualizations at first-order approximation, of a single message m posted by user u at time t .”
The paper relies on this without support.
The geolocated subset of tweets is representative enough of the full Twitter sample for estimating infodemic risk.
“The fraction of Geoolocated (messages with shared locations, or geonamed, indicated in green) is constantly about 56% of the total volume recorded”
The authors state this as an assumption.
Message reliability can be adequately represented as a binary value after discarding links that cannot be easily assessed.
“The reliability r m of a single message m is either 0 or 1, because we discard all web links that can not be easily assessed”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Similar results were obtained about the presence of bots in online cryptocurrency discussions [24] and as part of the 'infodemics' about the COVID-19 pandemic [14].”
From A decade of social bot detection (2020) · cites this paper as [14]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
“Reliable news includes MSM and SCIENCE, whereas unreliable news includes the remaining categories.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
“OTHER 5 UNKNOWN Domains pointing to general content that can not be easily classified, such as videos on YouTube.SHADOW 6 UNKNOWN Domains related to URL shortening, that can be classified a priori .”
Citations
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Background
Cited by
Sorted by citations
What social media told us in the time of COVID-19: A scoping review
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“The study by Gallotti et al [33] also highlighted the differentiated roles of verified and unverified users on social media in propagating COVID-19–related information.”
InfluentialSocial media trust: Fighting misinformation in the time of crisis
Shahbazi, M., & Bunker, D. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102780 · Cited by 126
Sentiment analysis and topic modeling for COVID-19 vaccine discussions
Yin, H., Song, X., Yang, S., & Li, J. (2022) · World Wide Web · doi:10.1007/s11280-022-01029-y · Cited by 125
“[20] noticed that infodemic spread rapidly and widely through social media platforms during the pandemic.”
BackgroundLink, E. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102714 · Cited by 119
Statistical inference links data and theory in network science
Peel, L., Peixoto, T. P., & Domenico, M. D. (2022) · Nature Communications · doi:10.1038/s41467-022-34267-9 · Cited by 119
“…infrastructures 12,13 to communication systems 14,15 ) and social (from socio-technical relationships 16 – 19 to animal interactions 20,21 ) systems, enhancing our understanding of emergent phenomena in society 22,23 , life 24 – 26 and disease 27 – 29 , as well as their combination 30 – 35 .”
BackgroundSelf-citationRussian propaganda on social media during the 2022 invasion of Ukraine
Geissler, D., Bär, D., Pröllochs, N., & Feuerriegel, S. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00414-5 · Cited by 190
“Here, it may be likely that counter-measures from fake news mitigation can be adapted [50, 51]; yet this requires further research to establish the effectiveness of such interventions.”
BackgroundUyheng, J., & Carley, K. M. (2020) · Journal of Computational Social Science · doi:10.1007/s42001-020-00087-4 · Cited by 105
“To enforce reasonable comparability between the datasets, hashtags which were not geographically specific to either the US or the Philippines were not included, such as #Wuhanvirus, #Chinavirus, and #coronavirus, which related studies have otherwise used [36, 89].”
MethodPrevalence of Low-Credibility Information on Twitter During the COVID-19 OutbreakPreprint
Yang, K.-C., Torres-Lugo, C., & Menczer, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14484 · Cited by 124
“All rights reserved. behaviors of inauthentic actors (Gallotti et al. 2020; Ferrara 2020).”
BackgroundSu, Z., Cheshmehzangi, A., McDonnell, D., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.839433 · Cited by 94
Ruffo, G., Semeraro, A., Giachanou, A., & Rosso, P. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100531 · Cited by 130
Salvi, C., Iannello, P., Cancer, A., McClay, M., Rago, S., Dunsmoor, J. E., & Antonietti, A. (2021) · Frontiers in Communication · doi:10.3389/fcomm.2020.562588 · Cited by 100
“Recent studies on the impact of misinformation during the COVID19 pandemic highlight how unreliable and inflammatory information (Gallotti et al., 2020) may have threatened public health in several countries by altering individuals’ perception of risk behaviors (Oh et al.”
BackgroundWang, X., Fan, C., Yu, G., & Zhang, K. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107174 · Cited by 97
Shi, W., Liu, D., Yang, J., Zhang, J., Wen, S., & Su, J. Y. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17228701 · Cited by 96
“It has been well acknowledged that in the outbreak, non-human accounts contributed to social media infodemics [23,66,67], yet bots’ involvement in public sentiments during the pandemic remains unclear.”
BackgroundCompeting spreading dynamics in simplicial complex
Wen-yao, L., Xue, X., Pan, L., Lin, T., & Wang, W. (2021) · Applied Mathematics and Computation · doi:10.1016/j.amc.2021.126595 · Cited by 97
“[8] Riccardo Gallotti, Francesco Valle, Nicola Castaldo, Pierluigi Sacco, and Manlio De Domenico.”
BackgroundCountering misinformation: A multidisciplinary approach
Gradoń, K., Hołyst, J. A., Moy, W. R., Sienkiewicz, J., & Suchecki, K. (2021) · Big Data & Society · doi:10.1177/20539517211013848 · Cited by 102
“As Gallotti et al. (2020) noted on the verge of the global pandemic emergency, human communication is largely characterized by the production of informational noise and misleading or false information.”
BackgroundGarcía-Marín, D. (2020) · El Profesional de la Informacion · doi:10.3145/epi.2020.jul.11 · Cited by 106
Social Media for Public Health: Framework for Social Media–Based Public Health Campaigns
Hunt, I. D. V., & Linos, E. (2022) · Journal of Medical Internet Research · doi:10.2196/42179 · Cited by 109
“The extent to which social media facilitates dissemination of misinformation was exemplified by infodemics—defined by the World Health Organization as an overabundance of both inaccurate and accurate information—during the COVID-19 pandemic [20,21].”
Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 CrisisPreprint
Ziems, C., He, B., Soni, S., & Kumar, S. (2020) · arXiv (Cornell University) · Cited by 81
Vijaykumar, S., Jin, Y., Rogerson, D., Lu, X., Sharma, S., Maughan, A. D., Fadel, B., de Oliveira-Costa, M. S., Pagliari, C., & Morris, D. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00752-7 · Cited by 79
Papadopoulou, L., & Maniou, T. A. (2021) · Digital Journalism · doi:10.1080/21670811.2021.1945472 · Cited by 85
Cheng, M., Wang, S., Yan, X., Yang, T., Wang, W., Zehao, H., Xiao, X., Nazarian, S., & Bogdan, P. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.644801 · Cited by 80
One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study
Pierri, F., DeVerna, M., Yang, K.-C., Axelrod, D., Bryden, J., & Menczer, F. (2023) · Journal of Medical Internet Research · doi:10.2196/42227 · Cited by 78
“However, our aggregate analysis could not disentangle the infodemic trends and peaks associated with different countries as observed in prior work [29].”
Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data
Rao, S., Verma, A. K., & Bhatia, T. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119594 · Cited by 74
García-Saisó, S., Martí, M., Brooks, I. S., et al. (2021) · Revista Panamericana de Salud Pública · doi:10.26633/rpsp.2021.56 · Cited by 67
#COVID-19 on Twitter: Bots, Conspiracies, and Social Media Activism
Ferrara, E. (2020) · Europe PMC (PubMed Central) · Cited by 67
#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19
Stella, M., Restocchi, V., & Deyne, S. D. (2020) · Big Data and Cognitive Computing · doi:10.3390/bdcc4020014 · Cited by 63
Martínez, A. A., Torres, M. J. F., & Fernández, L. R. (2022) · Revista Latina de Comunicación Social · doi:10.4185/rlcs-2022-1538 · Cited by 64
Coordination patterns reveal online political astroturfing across the world
Schoch, D., Keller, F. B., Stier, S., & Yang, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-08404-9 · Cited by 64
Sustained online amplification of COVID-19 elites in the United States
Gallagher, R. J., Doroshenko, L., Shugars, S., Lazer, D., & Foucault Welles, B. (2021) · Social Media + Society · doi:10.1177/20563051211024957 · Cited by 80
“However, media outlets vary widely in their commitment and ability to sharing high-quality, accurate information, and the World Health Organization and other experts have warned of an “infodemic which has plagued the COVID-19 pandemic (W.H.O. 2020; Donovan 2020; Gallotti et al. 2020).”
BackgroundBotha, N. N., Segbedzi, C. E., Dumahasi, V. K., Maneen, S., Kodom, R. V., Tsedze, I. S., Akoto, L. A., Atsu, F. S., Lasim, O. U., & Ansah, E. W. (2024) · Archives of Public Health · doi:10.1186/s13690-024-01414-1 · Cited by 81
Vertical social distancing policy is ineffective to contain the COVID-19 pandemic
Duczmal, L. H., Almeida, A. C. L., Duczmal, D. B., Alves, C. R. L., Magalhães, F. C. O., de Lima, M. S., Silva, I. R., & Takahashi, R. H. C. (2020) · Cadernos de Saúde Pública · doi:10.1590/0102-311x00084420 · Cited by 65
Adebesin, F., Smuts, H., Mawela, T., Maramba, G., & Hattingh, M. (2023) · JMIR Infodemiology · doi:10.2196/48620 · Cited by 57
“24 Nature Human Behaviour 205 Assessing the Risks of “Infodemics” in Response to COVID19 Epidemics Gallotti et al [50], 2020”
Causal Modeling of Twitter Activity during COVID-19
Gencoglu, O., & Gruber, M. (2020) · Computation · doi:10.3390/computation8040085 · Cited by 57
Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media
Solovev, K., & Pröllochs, N. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512266 · Cited by 74
“On the other hand, however, this crisis has bred a multitude of rumors [24, 26, 33, 36, 45], and verdicts of reputable fact-checking organizations (e.”
BackgroundFlow of online misinformation during the peak of the COVID-19 pandemic in Italy
Caldarelli, G., Nicola, R. D., Petrocchi, M., Pratelli, M., & Saracco, F. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00289-4 · Cited by 60
Vijaykumar, S., McNeill, A., & Simpson, J. (2021) · Public Health Nutrition · doi:10.1017/s1368980021000124 · Cited by 55
“The public concern about CNI, as evidenced by these examples, is problematic when the current context is considered: the UK faces an obesity crisis and therefore requires the majority of the population to make critical dietary choices; and social media, the most preferred source of nutrition information, is now rife w…”
BackgroundCollective emotions during the COVID-19 outbreak.
Metzler, H., Rimé, B., Pellert, M., Niederkrotenthaler, T., Natale, A. D., & García, D. (2022) · Emotion · doi:10.1037/emo0001111 · Cited by 59
A multilayer network model of the coevolution of the spread of a disease and competing opinions
Peng, K., Zheng, L., Lin, V., Lindstrom, M. R., Parkinson, C., Wang, C., Bertozzi, A. L., & Porter, M. A. (2021) · Mathematical Models and Methods in Applied Sciences · doi:10.1142/s0218202521500536 · Cited by 63
“On the other hand, social media also allows the pervasive spread of misinformation and disinformation, leading to so-called infodemics, which refer to epidemics of information.(27,73) Distortion and inaccurate information can impair people’s mental and physical health,(63) trigger anxiety and distrust, and ultimately…”
BackgroundBayesian Graph Local Extrema Convolution with Long-tail Strategy for Misinformation Detection
Zhang, G., Zhang, S., & Yuan, G. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3639408 · Cited by 57
“[15].”
Community notes increase trust in fact-checking on social media
Drolsbach, C., Solovev, K., & Pröllochs, N. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae217 · Cited by 82
Two Stage Transformer Model for COVID-19 Fake News Detection and Fact CheckingPreprint
Vijjali, R., Potluri, P., Kumar, S., & Teki, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13253 · Cited by 93
“In (Gallotti et al., 2020), the authors develop an Infodemic Risk Index (IRI) after analyzing Twitter posts”
BackgroundShams, A. B., Apu, E. H., Rahman, A., Raihan, M. M. S., Siddika, N., Preo, R. B., Hussein, M. R., Mostari, S., & Kabir, R. (2021) · Healthcare · doi:10.3390/healthcare9020156 · Cited by 58
“7 million tweets on COVID-19 streaming past every day [13].”
BackgroundInformation disorders during the COVID-19 infodemic: The case of Italian Facebook
Guarino, S., Pierri, F., Giovanni, M. D., & Celestini, A. (2021) · Online Social Networks and Media · doi:10.1016/j.osnem.2021.100124 · Cited by 66
Cruickshank, I. J., & Carley, K. M. (2020) · Applied Network Science · doi:10.1007/s41109-020-00317-8 · Cited by 54
“This is because good information is a key enabler to combat the effects of the pandemic whereas misinformation can exacerbate its effects (Hussain 2020; Gallotti et al. 2020).”
BackgroundCharacterizing network dynamics of online hate communities around the COVID-19 pandemic
Uyheng, J., & Carley, K. M. (2021) · Applied Network Science · doi:10.1007/s41109-021-00362-x · Cited by 65
Living with COVID-19-triggered pseudoscience and conspiracies
Desta, T. T., & Mulugeta, T. (2020) · International Journal of Public Health · doi:10.1007/s00038-020-01412-4 · Cited by 51
Luo, H., Meng, X., Zhao, Y., & Cai, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107733 · Cited by 55
“In addition, considering that the data used in this study were related to the COVID-19 epidemic, the number of daily confirmed cases was also included in the analysis as a control variable to reduce the possible influence of environmental factors (Gallotti et al., 2020).”
BackgroundMethodHow older adults manage misinformation and information overload - A qualitative study
Vivion, M., Reid, V., Dubé, È., Coutant, A., Benoit, A., & Tourigny, A. (2024) · BMC Public Health · doi:10.1186/s12889-024-18335-x · Cited by 55
“Information overload has essentially been studied in relation to the issues of misinformation and disinformation, rather than as a separate phenomenon [4, 5].”
BackgroundKumar, V., Singh, D., Kaur, M., & Damaševičius, R. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.564 · Cited by 50
“Gallotti et al. (2020) explored the social media tweets posted on Twitter.”
BackgroundAzithromycin consumption during the COVID-19 pandemic in Croatia, 2020
Bogdanić, N., Močibob, L., Vidović, T., Soldo, A., & Begovać, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263437 · Cited by 50
“analyzed Twitter messages posted from January to March 2020, and found an increase in potentially unreliable information in those early stages of the COVID-19 pandemic [8].”
BackgroundBots Amplify and Redirect Hate Speech in Online Discourse About Racism During the COVID-19 Pandemic
Uyheng, J., Bellutta, D., & Carley, K. M. (2022) · Social Media + Society · doi:10.1177/20563051221104749 · Cited by 53
“These issues play out in important ways in cyberspace, where “infodemics” have spread alongside COVID-19 outbreaks in the offline world (Gallotti et al., 2020).”
BackgroundThe role of social media in public health crises caused by infectious disease: a scoping review
Terry, K., Yang, F. H., Yao, Q., & Liu, C. (2023) · BMJ Global Health · doi:10.1136/bmjgh-2023-013515 · Cited by 53
“Therefore, maintaining a balance between the collection and use of public health information and the protection of citizens’ privacy has always been an important ethical and legal challenge for public health agencies and those studying these topics.”
BackgroundKestilä‐Kekkonen, E., Koivula, A., & Tiihonen, A. (2022) · European Political Science Review · doi:10.1017/s1755773922000224 · Cited by 44
“Having said that, citizens’ perspective of the ‘knowledge’ about the virus itself, the disease it causes, and prevention of and recovery from the disease are multidimensional and contradictory (Gallotti et al., 2020; Naeem and Bhatti, 2020).”
BackgroundDark Web Marketplaces and COVID-19: before the vaccine
Bracci, A., Nadini, M., Aliapoulios, M., McCoy, D., Gray, I., Teytelboym, A., Gallo, A., & Baronchelli, A. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00259-w · Cited by 53
Castioni, P., Andrighetto, G., Gallotti, R., di Sorrentino, E. P., & Domenico, M. D. (2022) · Royal Society Open Science · doi:10.1098/rsos.220716 · Cited by 43
“, refusing vaccines or not adopting non-pharmaceutical interventions, such as wearing masks and physical distancing) [13] that substantially increase epidemic spread [8, 14, 15].”
BackgroundMethodSelf-citationTsoy, D., Godinić, D., Tong, Q., Obrenovic, B., Khudaykulov, A., & Kurpayanidi, K. I. (2022) · Sustainability · doi:10.3390/su14127192 · Cited by 52
A multi-information epidemic spreading model on a two-layer network
Li, D., Xie, W., Han, D., et al. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119723 · Cited by 43
Misinformation and Hate Speech: The Case of Anti-Asian Hate Speech During the COVID-19 Pandemic
Kim, J. Y., & Kesari, A. (2021) · Journal of Online Trust and Safety · doi:10.54501/jots.v1i1.13 · Cited by 45
“Although the work on misinformation (e.g., Swire-Thompson and Lazer 2019; Jerit and Zhao 2020; Gallotti et al. 2020; Kouzy et al. 2020) and hate speech has been conducted in isolation (e.g., Howard 2019; Coates 2020), we argue that in practice, these two forms of online speech are interwoven.”
BackgroundLoubere, L., Kasparian, S., & Ashkar, F. (2018) · Les cahiers du journalisme · doi:10.31188/cajsm · Cited by 42
Health Misinformation on Social Media: A Systematic Literature Review and Future Research Directions
Li, Y., Marga, J. J., Cheung, C. M. K., Shen, X., & Lee, M. (2022) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00164 · Cited by 43
Vaccination passports: Challenges for a future of air transportation
Sun, X., Wandelt, S., & Zhang, A. (2021) · Transport Policy · doi:10.1016/j.tranpol.2021.06.018 · Cited by 41
“…argument for such counter-intuitive behaviour is that COVID-19 has put a magnifying glass on the fact that we are living in the age of infodemics (Gallotti et al., 2020), where few people or organizations intentionally spread fake news and false scientific claims, mostly under exploitation of…”
BackgroundMEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management
Hang, C. N., Yu, P.-D., Chen, S., Tan, C. W., & Chen, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3314632 · Cited by 51
“For instance, the authors in [6] introduced the Infodemic Risk Index (IRI) to evaluate the likelihood of an infodemic occurrence in various nations.”
BackgroundMethodInfluentialRetractedSystematic Literature Review of Social Media Bots Detection Systems
Ellaky, Z., Benabbou, F., & Ouahabi, S. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.04.004 · Cited by 38
Shapiro, J. T., Víquez‐R, L., Leopardi, S., Vicente‐Santos, A., Mendenhall, I. H., Frick, W. F., Kading, R. C., Medellín, R. A., Racey, P. A., & Kingston, T. (2021) · Viruses · doi:10.3390/v13071356 · Cited by 43
“The public and policy-makers are generally not reading academic journals, but getting information from sound bites, videos, and interviews disseminated via traditional news outlets and social media [207–209], both of which can spread misinformation [210,211].”
BackgroundCognitive underpinnings of COVID-19 vaccine hesitancy
Acar-Burkay, S., & Cristian, D. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114911 · Cited by 38
Xu, C. L., & Yan, W. (2022) · Current Psychology · doi:10.1007/s12144-022-03289-3 · Cited by 45
“This is not only because of the epidemic itself, but also because of the overwhelming information given during the epidemic (Catedrilla et al., 2020; Gallotti et al., 2020; Patel et al., 2020; Patwa et al., 2021).”
BackgroundDesinformación anticientífica sobre la COVID-19 difundida en Twitter en Hispanoamérica
García-Marín, D., & Merino-Ortego, M. (2022) · Cuadernos info · doi:10.7764/cdi.52.42795 · Cited by 37
Public health challenges and opportunities after COVID-19
Sacco, P. L., & Domenico, M. D. (2021) · Bulletin of the World Health Organization · doi:10.2471/blt.20.267757 · Cited by 35
“At the same time, fringe but very vocal social circles such as groups opposed to vaccination and more generally believers in conspiracy theories have lost momentum in the broader public opinion.(21) However, not all changes have been for the good.”
BackgroundSelf-citationLu, X., Vijaykumar, S., Jin, Y., & Rogerson, D. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101750 · Cited by 38
When Do Parties Lie? Misinformation and Radical-Right Populism Across 26 Countries
Törnberg, P., & Chueri, J. (2025) · The International Journal of Press/Politics · doi:10.1177/19401612241311886 · Cited by 74
“MBFC offers the largest dataset covering biased and low factual news sources, and has been widely used to identify misinformation shared on social media (e.g., Baly et al., 2018; Gallotti et al., 2020; Sharma et al., 2020).”
Ding, X., Shang, B., Xie, C., Xin, J., & Yu, F. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04564-x · Cited by 44
“On February 2, 2020, the World Health Organization (WHO) highlighted the “ infodemic ” as a parallel crisis to the pandemic and a signi fi cant barrier to effective public health responses (Gallotti et al. 2020; Naeem and Bhatti 2020; World Health Organization 2020).”
Bashaddadh, O. M. A., Omar, N., Mohd, M., & Khalid, M. N. A. (2025) · IEEE Access · doi:10.1109/access.2025.3572051 · Cited by 44
“Studies have also demonstrated a substantial increase in research efforts related to fake news detection and the development of detection systems during this period [63], [64].”
Characterizing the roles of bots on Twitter during the COVID-19 infodemic
Xu, W., & Sasahara, K. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00139-3 · Cited by 36
“Research about the COVID-19 infodemic has discovered the information flow pathways between humans and bots [14].”
BackgroundChen, E., Jiang, J., Chang, H.-C. H., Murić, G., & Ferrara, E. (2021) · JMIR Infodemiology · doi:10.2196/32378 · Cited by 38
“While the intensity of its effects varies based on country and culture, infodemics was and continues to be a salient issue in COVID-19 discourse [4,5].”
Studying the COVID-19 infodemic at scale
Gruzd, A. A., Domenico, M. D., Sacco, P. L., & Briand, S. C. (2021) · Big Data & Society · doi:10.1177/20539517211021115 · Cited by 36
Self-citationPierri, F., Luceri, L., Chen, E., & Ferrara, E. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00420-7 · Cited by 40
“An “infodemic” of misleading and inaccurate information became particularly worrisome during the COVID-19 pandemic (Cinelli et al. 2020b; Gallotti et al. 2020; Yang et al. 2021; Chen et al. 2022).”
Background
Metadata
- Authors
- Gallotti, R., Valle, F., Castaldo, N., Sacco, P., & De Domenico, M.
- Affiliations
- Fondazione Bruno Kessler (IT)
- Venue
- Nature Human Behaviour
- Published
- 2020-10-29
- Volume
- 4
- Issue
- 12
- Pages
- 1285-1293
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3015622078
- Cited by
- 591
- References
- 61
- Citations by year
- 2026: 39 · 2025: 69 · 2024: 80 · 2023: 90 · 2022: 129 · 2021: 137 · 2020: 46
- FWCI
- 134.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion, public-domain)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- infodemic, COVID-19 pandemic, misinformation, Twitter, news reliability, early warning indicators, epidemic spread, misinformation mitigation, public health communication, social media
- MeSH terms
- COVID-19, Humans, Mass Media, Models, Theoretical, Risk Assessment, Consumer Health Information, Social Networking, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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