The use of bot messaging, that being artificially created messages, has increased since 2010. While not all bots are bad, many have been used to share extreme and divisive views on a range of topics, from policy discussion to brand electronic word of mouth. The issue with bot messaging and its prevalence is that it can affect researchers’ understanding of a topic. For example, if 25% of a dataset is fabricated, decision-making may result in a loss of profit or poor policy formation. To counteract the use of bots, this research note offers a framework to alleviate the potentially destructive nature of bot data and ensure the cleaning of data is thorough and beneficial to decision-making based on social media commentary. The framework is a four-step process, which includes thematic, automated, and characteristic identification stages. We provide three case studies to demonstrate the approach and conclude by providing key practical implications.
AllemJ. P.FerraraE.UppuS. P.CruzT. B.UngerJ. B. (2017). E-cigarette surveillance with social media data: Social bots, emerging topics, and trends. JMIR Public Health and Surveillance, 3(4), 98–102.
2.
BollenJ.MaoH.ZengX. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.
BozdagE.van den HovenJ. (2015). Breaking the filter bubble: Democracy and design. Ethics and Information Technology, 17, 249–265. https://doi.org/10.1007/s10676-015-9380-y
5.
CaiC.LiL.ZengiD. (2017, July). Behavior enhanced deep bot detection in social media [Conference session]. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), Beijing, China.
6.
CrabbE. S.MishlerA.PaletzS.HefrightB.GolonkaE. (2015). Reading between the lines: A prototype model for detecting Twitter sockpuppet accounts using language-agnostic processes. In StephanidisC. (Ed.), International Conference on Human-Computer Interaction (pp. 656–661). Springer.
7.
CreswellJ. W.PothC. N. (2016). Qualitative inquiry and research design: Choosing among five approaches. SAGE.
8.
DanielF.CappielloC.BenatallahB. (2019). Bots acting like humans: Understanding and preventing harm. IEEE Internet Computing, 23(2), 40–49.
9.
DobsonA. (2014). Listening for democracy: Recognition, representation, reconciliation. Oxford University Press.
10.
FerraraE. (2020). Bots, elections, and social media: A brief overview. In ShuK.WangS.LeeD.LiuH. (Eds.), Lecture notes in social networks: Disinformation, misinformation, and fake news in social media (pp. 95–114). Springer. https://doi.org/10.1007/978-3-030-42699-6_6
11.
FerraraE.VarolO.DavisC.MenczerF.FlamminiA. (2016). The rise of social bots. Communications of the ACM, 59(7), 96–104.
12.
FreelonD.MarwickA.KreissD. (2020). False equivalencies: Online activism from left to right. Science, 369(6508), 1197–1201.
13.
FrenchJ.GordonR. (2019). Strategic social marketing: For behaviour and social change. SAGE.
14.
GrahamT.BrunsA.AngusD.HurcombeE.HamesS. (2020). #IStandWithDan versus #DictatorDan: The polarised dynamics of Twitter discussions about Victoria’s COVID-19 restrictions. Media International Australia, 179(1), 127–148. https://doi.org/10.1177/1329878X20981780
15.
HubertR. B.EstevezE.MaguitmanA.JanowskiT. (2020). Analysing and visualising government-citizen interactions on Twitter to support public policy-making. Digital Government: Research and Practice, 1(2), 1–20.
16.
HuiP. M.YangK. C.Torres-LugoC.MonroeZ.McCartyM.SerretteB. D.. . . MenczerF. (2019). Botslayer: Real-time detection of bot amplification on Twitter. Journal of Open Source Software, 4(42), 1706.
17.
KozinetsR. V. (2019). Netnography: The essential guide to qualitative social media research. SAGE.
18.
KuduguntaS.FerraraE. (2018). Deep neural networks for bot detection. Information Sciences, 467, 312–322.
LiuB. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2(2010), 627–666.
21.
MacnamaraJ. (2016). Organisational listening: The missing essential in public communication. Peter Lang.
22.
MehmetM. M. I.SimmonsP. (2019). Operationalising social media in upstream social marketing. Journal of Social Marketing, 9(3), 288–308.
23.
PariserE. (2011). The filter bubble: What the Internet is hiding from you. Penguin Books.
24.
ReuterK.LeeD. (2019). Evaluating patients’ perspectives on social media: The importance of clearly reporting data search, cleaning and processing. British Journal of Dermatology, 181(1), 222.
25.
SaldañaJ. (2021). The coding manual for qualitative researchers. SAGE.
26.
Sánchez-NúñezP.De Las Heras-PedrosaC.PeláezJ. I. (2020). Opinion mining and sentiment analysis in marketing communications: A science mapping analysis in Web of Science (1998–2018). Social Sciences, 9(3), 23.
WangG.MohanlalM.WilsonC.WangX.MetzgerM.ZhengH.ZhaoB. Y. (2012). Social turing tests: Crowdsourcing Sybil detection. NDSS, The Internet Society. https://arxiv.org/pdf/1205.3856.pdf
29.
WiesenbergM.TenchR. (2020). Deep strategic mediatisation: Organisational leaders’ knowledge and usage of social bots in an era of disinformation. International Journal of Information Management, 51, 102042.
30.
YangK. C.VarolO.HuiP. M.MenczerF. (2020). Scalable and generalizable social bot detection through data selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 1096–1103.
