With 1.3 billion users, Instagram (IG) has also become a business tool. IG
influencer marketing, expected to generate 33.25billionin2022,encouragescompaniesandinfluencerstocreatetrendingcontent.Variousmethodshavebeenproposedforpredictingapost′spopularity,i.e.,howmuchengagement(e.g.,Likes)itwillgenerate.However,thesemethodsarelimited:first,theyfocusonforecastingthelikes,ignoringthenumberofcomments,whichbecamecrucialin2021.Secondly,studiesoftenusebiasedorlimiteddata.Third,researchersfocusedonDeepLearningmodelstoincreasepredictiveperformance,whicharedifficulttointerpret.Asaresult,end−userscanonlyestimateengagementafterapostiscreated,whichisinefficientandexpensive.AbetterapproachistogenerateapostbasedonwhatpeopleandIGlike,e.g.,byfollowingguidelines.Inthiswork,weuncoverpartoftheunderlyingmechanismsdrivingIGengagement.Toachievethisgoal,werelyonstatisticalanalysisandinterpretablemodelsratherthanDeepLearning(black−box)approaches.Weconductextensiveexperimentsusingaworldwidedatasetof10millionpostscreatedby34Kglobalinfluencersinninedifferentcategories.Withoursimpleyetpowerfulalgorithms,wecanpredictengagementupto94makinguscomparableandevensuperiortoDeepLearning−basedmethod.Furthermore,weproposeanovelunsupervisedalgorithmforfindinghighlyengagingtopicsonIG.Thankstoourinterpretableapproaches,weconcludebyoutliningguidelinesforcreatingsuccessfulposts.