Linguistic Signature of Superspreaders

This chapter examines how conspiracy theories spread by identifying the linguistic signatures of superspreaders—actors who disproportionately amplify content within online networks. Integrating diffusion theory, network analysis, and computational linguistics, the chapter shows that superspreading is shaped not only by network position and past influence but also by language choice. Using COVID-19 conspiracy theories as the empirical setting, the analysis demonstrates that while emotional and negative language (especially anger and disgust) characterizes influential communication during crises, conspiracy theories themselves superspread most effectively when framed in neutral, rational-sounding language. Superspreaders of conspiracy theories use fewer emotional cues, less toxic language, and more complex syntax than both ordinary users and general superspreaders. This linguistic neutrality allows conspiracy theories to function as credible “solutions” to fear rather than as emotional reactions to it. The findings reconceptualize superspreading as a semantic as well as structural process, highlighting how language enables conspiracy theories to travel widely and gain legitimacy within online communities.