Persuasion aims at affecting the audience’s attitude and behaviour through a series of messages containing persuasion strategies. In the context of misinformation spread, identifying the persuasion strategies is important in order to warn people to be aware of the analogous persuasion attempts in the future. In this work, we address the prediction of persuasion strategies in micro-blogging posts through a multi-label classification approach based on a variety of lexical and semantic features. We conduct our experiments using a set of well-known multi-label classification algorithms, including multi-label decision tree, multi-label k-nearest neighbours, multi-label random forest, binary relevance and classifier chains. The results show that the model incorporating classifier chains and XGBoost algorithm achieves the best subset accuracy of 0.779 and the highest macro F1-score of 0.847. In addition, we evaluated and compared the features’ importance for different persuasion strategies and analysed the major errors of miss-out prediction. The findings of this article provide a benchmark for the multi-label classification of persuasion strategies in micro-blogging posts and lead to a better understanding of different persuasion attempts contained in social media misinformation.
MurphyJJ. Rhetoric in the middle ages: a history of rhetorical theory from Saint Augustine to the Renaissance. Berkeley, CA: University of California Press, 1981, p. 4.
2.
SimonsH. Persuasion in society. 2nd ed.New York: Routledge, 2011, p. 24.
3.
XiaoL. Fighting disinformation in social media: an online persuasion perspective. In: LuttrellRXiaoLGlassJ (eds) Democracy in the disinformation age: influence and activism in American politics. 1st ed. New York: Routledge, 2021, pp. 201–220.
4.
ZhouCLiKLuY. Linguistic characteristics and the dissemination of misinformation in social media: the moderating effect of information richness. Inform Process Manage2021; 58(6): 102679.
5.
FoggBJ. Mass interpersonal persuasion: an early view of a new phenomenon. In: Proceedings of the international conference on persuasive technology, Oulu, 4–6 June 2008, pp. 23–34. Berlin: Springer.
6.
BronsteinJ. Like me! Analyzing the 2012 presidential candidates’ Facebook pages. Online Inf Rev2013; 37(2): 173–192.
7.
AugerGA. Rhetorical framing: examining the message structure of nonprofit organizations on Twitter. Int J Nonprofit Volunt Sect Mark2014; 19(4): 239–249.
8.
AlhabashSMcAlisterARHagerstromA, et al. Between likes and shares: effects of emotional appeal and virality on the persuasiveness of anticyberbullying messages on Facebook. Cyberpsychol Behav Soc Netw2013; 16(3): 175–182.
9.
PangNLawPW. Retweeting #WorldEnvironmentDay: a study of content features and visual rhetoric in an environmental movement. Comput Hum Behav2017; 69: 54–61.
10.
ChenSXiaoLMaoJ. Persuasion strategies of misinformation-containing posts in the social media. Inform Process Manage2021; 58(5): 102665.
11.
HuberBTauschSHußmannH. Supporting debates with a real-time feedback system. In: Proceedings of the CHI’14 extended abstracts on human factors in computing systems, Toronto, ON, Canada, 26 April–1 May 2014, pp. 2257–2262. New York: ACM.
12.
ChangASchulzPJJiaoW, et al. Media source characteristics regarding food fraud misinformation according to the health information national trends survey (HINTS) in China: comparative study. JMIR Format Res2022; 6(3): e32302.
13.
LeeJOttTDeavoursD. Combating misinformation in risk: emotional appeal in false beliefs. In: LuttrellRXiaoLGlassJ (eds) Democracy in the disinformation age: influence and activism in American politics. 1st ed. New York: Routledge, pp. 165–181.
14.
WangRHeYXuJ, et al. Fake news or bad news? Toward an emotion-driven cognitive dissonance model of misinformation diffusion. Asian J Commun2020; 30(5): 317–342.
15.
ChenSXiaoLKumarA. Spread of misinformation on social media: what contributes to it and how to combat it. Comput Hum Behav2023; 141: 107643.
16.
NyilasyG. Fake news: when the dark side of persuasion takes over. Int J Advert2019; 38(2): 336–342.
17.
ShuKSlivaAWangS, et al. Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newslett2017; 19(1): 22–36.
18.
GarrettRK. Social media’s contribution to political misperceptions in U.S. Presidential elections. PLoS ONE2019; 14(3): e0213500.
19.
EckerUKHLewandowskySCookJ, et al. The psychological drivers of misinformation belief and its resistance to correction. Nat Rev Psychol2022; 1(1): 13–29.
20.
MurungiDPuraoSYatesD. Beyond facts: a new spin on fake news in the age of social media. In: Proceedings of the twenty-fourth Americas conference on information systems, New Orleans, LA, 16–18 August 2018, pp. 1–10. Atlanta, GA: Association for Information Systems.
21.
SerefMMHSerefO. Rhetoric mining for fake news: identifying moves of persuasion and disinformation. In: Proceedings of the 25th Americas conference on information systems, Cancún, Mexico, 15–17 August 2019.
22.
ShaoY. Ethos, logos, pathos: strategies of persuasion in social/environmental reports. Soc Environ Account J2013; 33(3): 179–180.
23.
ChenHGhosalDMajumderN, et al. Persuasive dialogue understanding: the baselines and negative results. Neurocomputing2021; 431: 47–56.
24.
TsoumakasGKatakisI. Multi-label classification: an overview. Int J Data Warehous Min2007; 3(3): 1–13.
25.
ReadJPfahringerBHolmesG, et al. Classifier chains for multi-label classification. Mach Learn2011; 85(3): 333–359.
26.
SzymanskiPKajdanowiczT. Scikit-multilearn: a scikit-based Python environment for performing multi-label classification. J Mach Learn Res2019; 20(1): 209–230.
27.
ChenLTangH. Examining the persuasion process of narrative fear appeals on health misinformation correction. Inform Commun Soc. Epub ahead of print 10October2022. DOI: 10.1080/1369118X.2022.2128849.
28.
SharotT. To quell misinformation, use carrots –not just sticks. Nature2021; 591(7850): 347.
29.
IyerRRSycaraKPLiY. Detecting type of persuasion: is there structure in persuasion tactics? In: Proceedings of the 18th workshop on computational models of natural argument, London, 16 July 2017, pp. 54–64. Stroudsburg, PA: Association for Computational Linguistics.
30.
HideyCMusiEHwangA, et al. Analyzing the semantic types of claims and premises in an online persuasive forum. In: Proceedings of the 4th workshop on argument mining, Copenhagen, 8 September 2017, pp. 11–21. Stroudsburg, PA: Association for Computational Linguistics.
31.
HideyCMcKeownK. Persuasive influence detection: the role of argument sequencing. In: Proceedings of the AAAI conference on artificial intelligence, New Orleans, LA, 2–7 February 2018, pp. 5173–5180. New York: Columbia University.
32.
YangDChenJYangZ, et al. Let?s make your request more persuasive: Modeling persuasive strategies via semi-supervised neural nets on crowdfunding platforms. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2–7 June, 2019, pp. 3620–3630. Stroudsburg, PA: Association for Computational Linguistics.
33.
WangXShiWKimR, et al. Persuasion for good: towards a personalized persuasive dialogue system for social good. In: Proceedings of the 57th annual meeting of the association for computational linguistics (ACL 2019), Florence, 28 July–2 August 2019, pp. 5635–5649. Stroudsburg, PA: Association for Computational Linguistics.
34.
MorioGEgawaRFujitaK. Revealing and predicting online persuasion strategy with elementary units. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), Hong Kong, China, 3–7 November 2019, pp. 6274–6279. Stroudsburg, PA: Association for Computational Linguistics.
35.
HabernalIGurevychI. Which argument is more convincing? Analyzing and predicting convincingness of web arguments using bidirectional LSTM. In: Proceedings of the 54th annual meeting of the association for computational linguistics, Berlin, 7–12 August 2016, pp. 1589–1599. Stroudsburg, PA: Association for Computational Linguistics.
36.
TanCNiculaeVDanescu-Niculescu-MizilC, et al. Winning arguments: interaction dynamics and persuasion strategies in good-faith online discussions. In: Proceedings of the 25th international conference on world wide web, Montreal, QC, Canada, 11–15 April 2016, pp. 613–624. New York: ACM.
37.
KhazaeiTXiaoLMercerR. Writing to persuade: analysis and detection of persuasive discourse. In: Proceedings of the iConference 2017, Wuhan, China, 22–25 March 2017.
38.
XiaoL. A message’s persuasive features in Wikipedia’s article for deletion discussions. In: Proceedings of the 9th international conference on social media and society, Copenhagen, 18–20 July 2018, pp. 345–349. New York: ACM.
39.
GhoshDKhanamAHanY, et al. Coarse-grained argumentation features for scoring persuasive essays. In: Proceedings of the 54th annual meeting of the association for computational linguistics, Berlin, 7–12 August 2016, pp. 549–554. Stroudsburg, PA: Association for Computational Linguistics.
40.
WeiZLiuYLiY. Is this post persuasive? Ranking argumentative comments in online forum. In: Proceedings of the 54th annual meeting of the association for computational linguistics (ACL 2016), Berlin, 7–12 August 2016, pp. 195–200. Stroudsburg, PA: Association for Computational Linguistics.
41.
MusiEGhoshDMuresanS. ChangeMyView through concessions: do concessions increase persuasion?Dialog Discourse2018; 9(1): 107–127.
42.
DuttaSDasDChakrabortyT. Changing views: persuasion modeling and argument extraction from online discussions. Inform Process Manage2020; 57(2): 102085.
43.
LiXZhangDWuB. Detection method of phishing email based on persuasion principle. In: Proceedings of the 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC), Chongqing, China, 12–14 June 2020, pp. 571–574. New York: IEEE.
44.
XiaoLKhazaeiT. Changing others’ beliefs online: online comments’ persuasiveness. In: Proceedings of the 10th international conference on social media and society, Toronto, ON, Canada, 19–21 July 2019, pp. 92–101. New York: ACM.
45.
TausczikYRPennebakerJW. The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol2010; 29(1): 24–54.
46.
McGlynnJBaryshevtsevMDaytonZA. Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths. Harvard Kennedy School Misinform Rev2020; 1(3): 1–16.
47.
BenderEMMorganJTOxleyM, et al. Annotating social acts: authority claims and alignment moves in Wikipedia talk pages. In: Proceedings of the workshop on language in social media, Portland, OR, 23 June 2011, pp. 48–57. Stroudsburg, PA: Association for Computational Linguistics.
48.
XiaoLAskinN. Academic opinions of Wikipedia and open access publishing. Online Inform Rev2014; 38(3): 332–347.
49.
ÖcalAXiaoLParkJ. Reasoning in social media: insights from Reddit ‘Change My View’ submissions. Online Inform Rev2021; 45(7): 1208–1226.
50.
BellBELoftusEF. Vivid persuasion in the courtroom. J Person Assess1985; 49(6): 659–664.
51.
ChakmaKDasA. A 5W1H based annotation scheme for semantic role labeling of English tweets. Comput Sistem2018; 22(3): 747–755.
52.
Bonet-JoverAPiad-MorffisASaqueteE, et al. Exploiting discourse structure of traditional digital media to enhance automatic fake news detection. Exp Syst Appl2021; 169: 114340.
53.
RehbeinI. On the role of discourse relations in persuasive texts. In: Proceedings of the 13th linguistic annotation workshop, Florence, 1 August 2019, pp. 144–154. Stroudsburg, PA: Association for Computational Linguistics.
54.
ZhangMSongYQinB, et al. Chinese discourse relation recognition. J Chin Inform Process2013; 27(6): 51–57.
55.
GibajaEVenturaS. A tutorial on multilabel learning. ACM Comput Surv2015; 47(3): 1–38.
56.
YappEKYLiXLuWF, et al. Comparison of base classifiers for multi-label learning. Neurocomputing2020; 394: 51–60.
57.
BogatinovskiJTodorovskiLDžeroskiS, et al. Comprehensive comparative study of multi-label classification methods. Exp Syst Appl2022; 203: 117215.
58.
BreimanL. Random forests. Machine Learning2001; 45(1): 5–32.
59.
ChenTGuestrinC. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, 13–17 August 2016, pp. 785–794. New York: ACM.
PereiraRBPlastinoAZadroznyB, et al. Correlation analysis of performance measures for multi-label classification. Inform Process Manage2018; 54(3): 359–369.
62.
MadjarovGKocevDGjorgjevikjD, et al. An extensive experimental comparison of methods for multi-label learning. Pattern Recogn2012; 45(9): 3084–3104.
63.
GhamrawiNMcCallumA. Collective multi-label classification. In: Proceedings of the 14th ACM international conference on Information and knowledge management, Bremen, 31 October 2005, pp.195–200. New York: ACM.
64.
SorowerMS. A literature survey on algorithms for multi-label learning. Technical Report, Oregon State University, Corvallis, OR, 2010.
65.
DevlinJChangMWLeeK, et al. BERT: pre-training of deep bidirectional transformers for language understanding, https://arxiv.org/abs/1810.04805#
XiaoLHuoX. Authority claim in rationale-containing online comments. In: Proceedings of the 14th international conference on information, Washington, DC, 31 March–3 April 2019, pp. 690–696. Cham: Springer.
68.
KreuterMWGreenMCCappellaJN, et al. Narrative communication in cancer prevention and control: a framework to guide research and application. Ann Behav Med2007; 33(3): 221–235.
69.
FeldmanMSSköldbergKBrownRN, et al. Making sense of stories: a rhetorical approach to narrative analysis. J Publ Admin Res Theory2004; 14(2): 147–170.
70.
BaldwinC. Narrative rhetoric in expert reports: a case study. Narrat Matters2011; 1(2): 3–20.
OuyangJMcKeownK. Towards automatic detection of narrative structure. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14), Reykjavik, 26–31 May 2014, pp. 4624–4631. Paris: European Language Resources Association.
73.
HillCAHelmersM. Defining visual rhetorics. 1st ed. New York: Routledge, 2004, pp. 41–59.
74.
ReyesA. Building intimacy through linguistic choices, text structure and voices in political discourse. Lang Commun2015; 43: 58–71.
75.
ZhuYMaLJiangR. A cross-cultural study of English and Chinese online platform reviews: a genre-based view. Discour Commun2019; 13(3): 342–365.
76.
SundararajanL. Understanding emotion in Chinese culture. New York: Springer International Publishing, 2015, pp. 39–55.
77.
MareAMabweazaraHMMoyoD. ‘Fake news’ and cyber-propaganda in Sub-Saharan Africa: recentering the research agenda. Afr J Stud2019; 40(4): 1–12.
78.
RampersadGAlthiyabiT. Fake news: acceptance by demographics and culture on social media. J Inform Technol Polit2020; 17(1): 1–11.
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