Detecting COVID-19 Fake News on Twitter: Followers,Emotions,Relationships,and Uncertainty

Abstract

Fake news about coronavirus disease 2019 (COVID-19) can discourage people from taking preventive measures (masks, social distancing), thereby increasing infections and deaths; thus, this study tests whether attributes of users or COVID-19 tweets can distinguish tweets of true news versus fake news. We analyzed 4,165 spell-checked English tweets with a link to 1 of 20 matched COVID-19 news stories (10 true and 10 fake), across the world during 1 year, via computational linguistics and advanced statistics. Tweets with common words, negative emotional valence, higher arousal, greater dominance, first person singular pronouns, third person pronouns or by users with more followers were more likely to be true news tweets. By contrast, tweets with second person pronouns, bald starts, or hedges were more likely to be fake news tweets. Accuracy (F1 score) was 95%. While some tweet attributes for detecting fake news might be universal (pronouns, politeness, followers), others might be topic specific (common words, emotions, hedges).

Keywords

deception writing politeness social media hedge

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References

Abdelminaam

D. S.

Ismail

F. H.

Taha

Houssein

E. H.

Nabil

(2021). COAID-DEEP: An optimized intelligent framework for automated detecting COVID-19 misleading information on twitter. IEEE Access, 9, 27840–27867.

Adolphs

Anderson

D. J.

(2018). The neuroscience of emotion: A new synthesis. Princeton University Press.

Al-Ahmad

Al-Zoubi

A. M.

Abu Khurma

Aljarah

(2021). An evolutionary fake news detection method for COVID-19 pandemic information. Symmetry, 13(6), 1091.

Avaaz (2020). Facebook’s algorithm: A major threat to public health. Avaaz.

Baxter

L. A.

(1984). An investigation of compliance-gaining as politeness. Human Communication Research, 10(3), 427–456. https://doi.org/10.1111/j.1468-2958.1984.tb00026.x

Benjamini

Krieger

A. M.

Yekutieli

(2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93(3), 491–507. https://doi.org/10.1093/biomet/93.3.491

Bertsekas

D. P.

(2014). Constrained optimization and Lagrange multiplier methods. Academic.

Bryk

A. S.

Raudenbush

S. W.

(1992). Hierarchical linear models. Sage.

Brysbaert

New

(2009). Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41(4), 977–990. https://doi.org/10.3758/BRM.41.4.977

10.

Brysbaert

Warriner

A. B.

Kuperman

(2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904–911. https://doi.org/10.3758/s13428-013-0403-5

11.

Ceron

de-Lima-Santos

M. F.

Quiles

M. G.

(2021). Fake news agenda in the era of COVID-19: Identifying trends through fact-checking content. Online Social Networks and Media, 21, 100116.

12.

Chen

Lerman

Ferrara

(2020). Tracking social media discourse about the COVID-19 pandemic: Development of a public coronavirus twitter data set. JMIR Public Health and Surveill, 6(2), e19273. https://doi.org/10.2196/19273

13.

Chiu

M. M.

(2008). Flowing toward correct contributions during groups’ mathematics problem solving: A statistical discourse analysis. Journal of the Learning Sciences, 17(3), 415–463. https://doi.org/10.1080/10508400802224830

14.

Chiu

M. M.

Y. W.

(2021). How fake news differs from personal lies. American Behavioral Scientist, 65(2), 243–258. https://doi.org/10.1177/0002764220910243

15.

Coxhead

(2000). A new academic word list. TESOL Quarterly, 34(2), 213–238. https://doi.org/10.2307/3587951

16.

Cummings

(2012). Scaring the public: Fear appeal arguments in public health reasoning. Informal Logic, 32(1), 25–50. https://doi.org/10.22329/il.v32i1.3146

17.

Danescu-Niculescu-Mizil

Sudhof

Jurafsky

Leskovec

Potts

(2013). A computational approach to politeness with application to social factors. arXiv preprint arXiv:1306.6078.

18.

de Marneffe

M.-C.

MacCartney

Manning

C. D

. (2006). Generating typed dependency parses from phrase structure parses [Conference session]. LREC (Vol. 6, pp. 449–454). May 24-26, 2006. Genoa, Italy.

19.

Dunbar

N. E.

Gangi

Coveleski

Adams

Bernhold

Giles

(2016). When is it acceptable to lie? Interpersonal and intergroup perspectives on deception. Communication Studies, 67(2), 129–146. https://doi.org/10.1080/10510974.2016.1146911

20.

Eelen

(2014). A critique of politeness theory. Routledge.

21.

Gifford

(2011). The dragons of inaction: Psychological barriers that limit climate change mitigation and adaptation. American Psychologist, 66(4), 290–302.

22.

Goldstein

(2011). Multilevel statistical models. Edward Arnold.

23.

Grunig

J. E.

Kim

J.-N.

(2017). Publics approaches to segmentation in health and risk messaging. In Parrott

(Ed.), Encyclopedia of health and risk message design and processing. Oxford University Press.

24.

Gundapu

Mamidi

(2021). Transformer based automatic COVID-19 fake news detection system. arXiv preprint arXiv:2101.00180.

25.

Institute for Strategic Dialogue (ISD) (2020). Far-right exploitation of COVID-19.

26.

Jarzabkowski

Sillince

J. A.

Shaw

(2010). Strategic ambiguity as a rhetorical resource for enabling multiple interests. Human Relations, 63(2), 219–248. https://doi.org/10.1177/0018726709337040

27.

Joreskog

Sorbom

(2018). LISREL 10.1. Scientific Software International.

28.

Kahn

(2006). The passive voice of science. In Fill

Muhlhausler

(Eds.), Ecolinguistics reader: Language, ecology and environment (pp. 241).

29.

Kennedy

(2008). A guide to econometrics. Wiley-Blackwell.

30.

Kim

J.-N.

Grunig

J. E.

(2011). Problem solving and communicative action: A situational theory of problem solving. Journal of Communication, 61(1), 120–149. https://doi.org/10.1111/j.1460-2466.2010.01529.x

31.

Kincaid

J. P.

Fishburne

R. P.

Jr. Rogers

R. L.

Chissom

B. S.

(1975). Derivation of new readability formulas (automated readability index, fog count and Flesch reading ease formula) for navy enlisted personnel. Naval Technical Training Command Millington TN Research Branch.

32.

King

Zeng

(2001). Logistic regression in rare events data. Political Analysis, 9, 137–163.

33.

Konstantopoulos

(2008). The power of the test for treatment effects in three-level cluster randomized designs. Journal of Research on Educational Effectiveness, 1(1), 66–88. https://doi.org/10.1080/19345740701692522

34.

Kwon

Cha

Jung

(2017). Rumor detection over varying time windows. PLoS One, 12(1), e016834. https://doi.org/10.1371/journal.pone.0168344

35.

Leonhardt

J. M.

Keller

L. R.

Pechmann

(2011). Avoiding the risk of responsibility by seeking uncertainty: Responsibility aversion and preference for indirect agency when choosing for others. Journal of Consumer Psychology, 21(4), 405–413. https://doi.org/10.1016/j.jcps.2011.01.001

36.

Little

T. D.

Card

N. A.

Bovaird

J. A.

Preacher

K. J.

Crandall

C. S.

(2012). Structural equation modeling of mediation and moderation with contextual factors. In Bovaird

Card

N. A.

(Eds.), Modeling contextual effects in longitudinal studies (pp. 207–230). Routledge.

37.

MacKinnon

D. P.

Lockwood

C. M.

Williams

(2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99–128.

38.

Martínez

I. A.

(2005). Native and non-native writers’ use of first person pronouns in the different sections of biology research articles in English. Journal of Second Language Writing, 14(3), 174–190. https://doi.org/10.1016/j.jslw.2005.06.001

39.

Moore

M. E.

(2001). Third person pronoun errors by children with and without language impairment. Journal of Communication Disorders, 34(3), 207–228. https://doi.org/10.1016/S0021-9924(00)00050-2

40.

Powers

D. M. W.

(2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies, 2(1), 37–63.

41.

Redlener

Sachs

J. D.

Hansen

Hupert

(2020). 130,000-210,000 avoidable covid-19 deaths—and counting—in the US. National Center for Disaster Preparedness, Columbia University.

42.

Rhodes

(2017). Fear-appeal messages: Message processing and affective attitudes. Communication Research, 44(7), 952–975. https://doi.org/10.1177/0093650214565916

43.

Roloff

M. E.

Janiszewski

C. A.

(1989). Overcoming obstacles to interpersonal compliance: A principle of message construction. Human Communication Research, 16(1), 33–61. https://doi.org/10.1111/j.1468-2958.1989.tb00204.x

44.

Sherman

D. K.

Kim

H. S.

(2002). Affective perseverance: The resistance of affect to cognitive invalidation. Personality and Social Psychology Bulletin, 28(2), 224–237. https://doi.org/10.1177/0146167202282008

45.

Stein

Buzcu-Guven

Dueñas-Osorio

Subramanian

Kahle

(2013). How risk perceptions influence evacuations from hurricanes and compliance with government directives. Policy Studies Journal, 41(2), 319–342. https://doi.org/10.1111/psj.12019

46.

Terry

Hayfield

Clarke

Braun

(2017). Thematic analysis. In W. S. Rogers (Ed.) The SAGE handbook of qualitative research in psychology (Vol. 2, pp. 17–37).

47.

Umphress

E. E.

Simmons

A. L.

Boswell

W. R.

Triana

M. D. C.

(2008). Managing discrimination in selection: The influence of directives from an authority and social dominance orientation. Journal of Applied Psychology, 93(5), 982–993. https://doi.org/10.1037/0021-9010.93.5.982

48.

Warriner

A. B.

Kuperman

Brysbaert

(2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45, 1191–1207. https://doi.org/10.3758/s13428-012-0314-x

49.

Wogalter

M. S.

Barlow

Murphy

S. A.

(1995). Compliance to owner’s manual warnings: Influence of familiarity and the placement of a supplemental directive. Ergonomics, 38(6), 1081–1091. https://doi.org/10.1080/00140139508925175