?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Incorporating+Emotions+into+Health+Mention+Classification+Task+on+Social+Media&rft.creator=Aduragba%2C+Olanrewaju+Tahir&rft.creator=Yu%2C+Jialin&rft.creator=Cristea%2C+Alexandra+I&rft.description=The+Health+Mention+Classification+(HMC)+task+plays+a+pivotal+role+in+leveraging+social+media+discourse+for+public+health+mention+monitoring%2C+particularly+in+identifying+and+tracking+disease+proliferation.+Despite+its+potential%2C+the+task+poses+significant+challenges%2C+due+to+the+nuanced+nature+of+health-related+discussion.+Building+upon+recent+insights+that+emotional+context+can+enhance+HMC+performance%2C+in+this+paper%2C+we+explore+how+affective+information+can+be+integrated+into+the+HMC+process.+Our+study+pioneers+two+distinct+methodological+pipelines+that+are+designed+to+embed+emotional+nuances+into+the+HMC+task%3A+(1)+a+two-stage+fine-tuning+process%2C+starting+with+an+implicit+affective+knowledge+injection+to+initialise+the+model%2C+followed+by+intermediate+HMC+task+fine-tuning%3B+and+(2)+an+explicit+multi-feature+fusion+strategy+to+leverage+affective+knowledge+for+HMC+prediction.+We+conducted+comprehensive+evaluations+across+five+diverse+HMC+benchmark+datasets%2C+encompassing+content+from+Twitter%2C+Reddit%2C+and+a+blend+of+other+social+media+platforms.+Our+empirical+findings+reveal+that+our+affective-enriched+models+achieve+statistically+significant+improvements+across+HMC+benchmarks.+Notably%2C+the+explicit+multi-feature+fusion+method+yielded+a+minimum+of+3%25+improvement+in+F1+score+over+established+BERT+baselines+across+all+tested+corpora.+Intriguingly%2C+our+analysis+also+suggests+that+the+exclusive+consideration+of+negative+emotional+indicators+does+not+detrimentally+impact+HMC+efficacy+compared+to+leveraging+both+positive+and+negative+emotions.+Furthermore%2C+our+affectiveness-aware+models+demonstrate+promising+utility+as+a+viable+alternative+in+scenarios+where+domain-specific+HMC+datasets+are+scarce+or+non-existent+for+fine-tuning+purposes.+The+consistent+performance+uplift+across+datasets+sourced+from+varied+social+media+channels+underscores+the+generalisability+and+resilience+of+our+proposed+framework%2C+marking+a+significant+step+forward+in+the+computational+understanding+of+health-related+conversations+in+the+digital+sphere.&rft.subject=health+mention+classification%2C+emotion+detection%2C+social+media%2C+natural+language+processing&rft.publisher=IEEE&rft.date=2024-01-22&rft.type=Proceedings+paper&rft.language=eng&rft.source=+++++In%3A++Proceedings+of+the+2023+IEEE+International+Conference+on+Big+Data+(BigData).++(pp.+pp.+4834-4842).++IEEE%3A+Sorrento%2C+Italy.+(2024)+++++&rft.format=text&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10187977%2F2%2FYu_2212.05039.pdf&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10187977%2F&rft.rights=open