Misinformation, disinformation, and fake news: lessons from an interdisciplinary, systematic literature review

Figures & data

Figure 1. Number of publications across time.

Table 1. Geographical distribution of articles.

Country	Frequency	Percentage
U.S.A.	436	34.6
U.K.	91	7.9
China	81	6.4
India	68	5.4
Spain	53	4.2
Australia	49	3.9
Canada	41	3.2
Italy	36	2.9
Brazil	33	2.6
Germany	30	2.4
Total	918	73.4

Note: In total we coded 69 countries, this table shows the 10 most coded ones and the respective percentage out of the whole sample.

Table 2. Number of publications across disciplines.

Discipline	Frequency	Percentage
Political Science	93	7.4
Communication	380	30.1
Psychology	106	8.4
Computer Science	235	18.6
Sociology	9	0.7
Philosophy	29	2.3
Economics	94	7.4
Engineering	31	2.5
Education	17	1.4
Health	82	6.5
Environmental Sciences	10	0.8
Information/Library Science	28	2.2
Other	126	9.9
Missing	21	1.7
Total	1261	100

Table 3. Distribution of publications across article types.

Article type	Frequency	Percentage
Theoretical/conceptual	239	19.0
Review	90	7.1
Empirical	932	73.9
Total	1261	100

Figure 2. Evolution of empirical themes across time.

Table 4. Distribution of methods.

Method	Frequency	Percentage
Survey	203	19.5
Interviews/focus groups	68	6.5
Experiment	211	20.3
Quantitative content analysis	168	16.1
Qualitative content analysis	98	9.4
Computational	293	28.2
Total	1041	100

Note: As methodological variables are not mutually exclusive, multiple methods were coded when applicable.

Table 5. Use of different types of media data.

Media type	Frequency	Percentage
Social media	141	56.4
Mainstream media	25	10.0
Alternative media	2	0.8
Fake media	11	4.4
Internet	71	28.4
Total	250	100

Note: As the media data variables are not mutually exclusive, multiple data types were coded when applicable.

Table 6. Distributions of themes across disciplines.

	Prevalence /Dissemination	Exposure	Effects	Correction	Strategic use	Detection /Characteristics	Solutions	processing	Total
Pol. Science	22	8	18	10	7	16	2	24	107
Communication	122	51	63	55	27	110	25	88	541
Psychology	11	3	26	32	0	13	1	71	157
Comp. Science	52	7	3	3	4	168	4	2	243
Economy	34	8	16	5	4	22	0	16	105
Health	35	11	15	4	1	22	5	6	99
Total	276	88	141	109	43	351	37	207	1252

Notes: As thematic variables were not mutually exclusive, multiple themes were coded when applicable. Only six most coded disciplines are shown.

Table 7. Distribution of methods across disciplines.

	Comp. methods	Qual. Content analysis	Quant. Content analysis	Experiment	Interview /focus group	Survey	Total
Pol. Science	5	10	12	23	2	17	69
Communication	21	45	76	83	38	87	350
Psychology	2	1	0	69	2	18	92
Comp. Science	180	1	10	1	2	11	205
Economy	22	6	8	16	6	21	79
Health	2	5	30	7	5	22	71
Total	232	68	136	199	55	176	866

Notes: As methods were not mutually exclusive, multiple methods were coded were applicable. Only six most coded disciplines are shown.

Table 8. Overview of key research gaps and research problems.

Theme	Research gap	Examples of key research questions
Detection	Testing computational methods beyond benchmarking tests and preexisting datasets in realistic circumstances. Individual differences in detection across different information contexts.	How can computational methods be used to detect misinformation, disinformation and/or fake news in real circumstances? How can computational (and other) methods be used to identify misinformation, disinformation and/or fake news across languages? To what extent are there differences in peoples’ ability to detect misinformation, disinformation and/or fake news regarding journalistic versus personal network settings?
Dissemination	Investigating the role of personal communication networks.	How do misinformation, disinformation and/or fake news travel through different parts of the information environment? To what extent is the dissemination of misinformation, disinformation and/or fake news concentrated among specific actors within the information environment (Twitter users, Facebook groups etc.)? What is the role of opinion leaders in the dissemination of misinformation, disinformation, and/orfake news?
Prevalence	Gauging prevalence beyond singular media outlets and/or topics and moving toward more generalizable findings.	How prevalent is misinformation, disinformation, and/or fake news in varying media types and outlets? Are there topics that demonstrate a particularly large share of misinformation, disinformation, and/or fake news, and if so, what characterize those topics? How has the prevalence of misinformation, disinformation, and/or fake news developed over time?
Characteristics	Identifying broader, underlying characteristics that are not tied to a specific topic or language.	Are the generalizable linguistic features of misinformation, disinformation, and/or fake news? What attributes characterize visual misinformation, disinformation, and/or fake news? What linguistic and stylistic characteristics does misinformation, disinformation, and/or fake news have in languages other than English?
Exposure	Investigating exposure in realistic circumstances as opposed to via self-reported measures as well as gaining a better understanding of the how, when, and where people are exposed to misinformation, disinformation, and/or fake news.	How can we gauge actual exposure to misinformation, disinformation, and/or fake news through, e.g. linkage studies or web traffic data? Under which circumstance are individuals more vulnerable to misinformation, disinformation, and/or fake news? To what extent do personal communication networks (online or offline) impact exposure to misinformation, disinformation, and/or fake news?
Impact	Analyzing impact/effects from a longitudinal perspective as well as moving toward the meso and macro level of analysis.	What is the impact of misinformation, disinformation, and/or fake news on outcome variables such as (mis)perceptions, attitudes, and political behavior over time? What is the impact of misinformation, disinformation, and/or fake news on e.g. issue publics, interest groups, social movements, or organizations? What is the impact of misinformation, disinformation, and/or fake news on the quality and character of the public debate?
Strategic use	Building a body of work that theorizes and empirically investigates the nature and dynamics of strategic use of misinformation, disinformation, and/or fake news.	Which types of actors utilize misinformation, disinformation, and/or fake news strategically and under which circumstances? How do different actors use misinformation, disinformation, and/or fake news strategically? (e.g. construction of narratives) For what purposes do different actors utilize misinformation, disinformation, and/or fake news strategically? (e.g. to convince, sow doubt, for political or economic gains)
Solutions	Examining strategies such as corrections or media literacy in realistic circumstances and explicitly as solutions as well as identifying and testing other strategies.	What is the impact of content moderation and fact-checking employed by social media corporations such as Facebook or Twitter in terms of limiting the dissemination and prevalence of misinformation, disinformation, and/or fake news? What is the impact of employed media literacy (or other) programs across varying geographical and socio-economic contexts in terms of decreasing vulnerability to misinformation, disinformation, and/or fake news? To what extent can computational social science in terms of algorithms for detection be leveraged as a means to limit prevalence and dissemination of misinformation, disinformation, and/or fake news?
Data
Quantitative vs. qualitative	Increasing qualitative insights to consolidate/challenge as well as supplement extant quantitative work.	What are people’s motivations to engage with misinformation, disinformation, and/or fake news (e.g. sharing online), and what does this engagement look like? How do individuals perceive the impact of misinformation, disinformation, and/or fake news on micro, meso and macro level across varying contexts? What are peoples’ strategies to detect misinformation, disinformation, and/or fake news across contexts?
Media data	Increasing the usage of varying types of media data, especially from mainstream and alternative media.	To what extent is alternative media a driver of dissemination and prevalence of misinformation, disinformation, and/or fake news? Is mainstream media a mitigating factor or can it lead to misperceptions via their coverage of misinformation, disinformation, and/or fake news? What are the differences between social media platforms in terms of prevalence, dissemination, and exposure to misinformation, disinformation, and/or fake news?
Geospatial distribution	Increasing studies using data from non-Western countries and outside of the U.S. more generally	Are there geographical differences across any of the research areas identified in this paper regarding misinformation, disinformation, and/or fake news? What factors at the system-level might explain differences with respect to any of the research areas identified in this paper regarding misinformation, disinformation, and/or fake news? How might different cultural norms influence the spreading of and vulnerability to misinformation, disinformation, and/or fake news?