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Do Events Change Opinions on Social Media? Studying the 2016 US Presidential Debates

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11864)

Abstract

Social media is the primary platform for discussions and reactions during various social events. Studies in this space focus on the aggregate opinion and sentiment analysis but fail to analyze the micro-dynamics. In this work, we present a case study of the 2016 US Presidential Debates, analyzing the user opinion micro-dynamics across the timeline. We present an opinion variation analysis coupled with micro and macro level user analysis in order to explain opinion change. We also identify and characterize varied user-groups derived through this analyses. We discover that aggregate change in opinion is better explained by the differential influx of polarized population rather than the change in individual’s stance or opinion.

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    Refer to Appendix A for the refined Hashtag list.

  2. 2.

    https://github.com/dennybritz/cnn-text-classification-tf.

  3. 3.

    Refer to Appendix B for results of CNN model on SemEval 2016 Task 6 test-set.

  4. 4.

    We use a 2-sample t-test to compare the population distributions.

  5. 5.

    https://www.debates.org/index.php?page=debate-transcripts. Refer to Appendix C and Appendix A for the Topic and Hashtag list respectively.

  6. 6.

    We refer the reader to the LIWC2015 development manual [17] for more information.

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Correspondence to Niyati Chhaya .

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Appendices

A Hashtag list

Following hashtags were used as distant supervision to annotate 3.8 million tweets from the Harvard Dataverse 2016 United States Presidential Election Tweet Ids Dataset [12]. Hashtags in Appendix A.1 and A.2 were combined to label tweets with stance in favor of Hillary (or equivalently against Trump). Whereas, hashtags in Appendixs A.3 and  A.4 were combined to label tweets with positive stance towards Trump (or equivalently negative stance towards Hillary).

1.1 A.1 Hashtags in favor of Hillary (+, Hillary)

hillaryforpr, imwithher2016, imwithhillary, hillaryforpresident, hillaryforamerica, hereiamwithher, estoyconella, hillarysopresidential, uniteblue, hillstorm2016, bluewave2016, welovehillary, itrusther, bluewave, hrcisournominee, itrusthillary, standwithmadampotus, momsdemandhillary, madamepresident, madampresident, imwither, herstory, republicans4hillary, hillarysoqualified, werewithher, vote4hillary, strongertogether, readyforhillary, hillafornia, unitedagainsthate, votehillary, wearewithher, republicansforhillary, hrc2016, connecttheleft, yeswekaine, voteblue2016, hillary2016, sheswithus, hillyes, iamwithher, heswithher, voteblue, hillaryaprovenleader, imwiththem, bernwithher, ohhillyes, imwithher, clintonkaine2016, whyimwithher, turnncblue, hillarystrong

1.2 A.2 Hashtags against Trump (-, Trump)

nevertrumppence, lgbthatestrumpparty, boycotttrump, orangehitler, wheresyourtaxes, poordonald, losertrump, notrumpanytime, dirtydonald, drumpf, trumpsopoor, nodonaldtrump, makedonalddrumpfagain, nastywomen, defeattrump, sleazydonald, weakdonald, unfittrump, trump20never, loserdonald, trumplies, dumbdonald, trumpliesmatter, releaseyourtaxes, crybabytrump, freethedelegates, lyingtrump, nastywomenvote, trumpleaks, stupidtrump, stoptrump, trumpthefraud, racisttrump, dumpthetrump, dumptrump, anyonebuttrump, wherertrumpstaxes, crookeddonald, treasonoustrump, antitrump, nevertrump, notrump, womentrumpdonald, nevergop, donthecon, crookeddrumpf, traitortrump, showusyourtaxes, trumptrainwreck, lyingdonald, crookedtrump, lyindonald, ripgop, trumptreason, lyintrump, chickentrump

1.3 A.3 Hashtags against Hillary (-, Hillary)

hillarysolympics, hillaryforprison, hillaryforprison2016, moretrustedthanhillary, heartlesshillary, neverclinton, handcuffhillary, queenofcorrupton, crookedhiliary, nomoreclintons, hillary4jail, fbimwithher, clintoncrimefamily, hillno, queenofcorruption, hillarylostme, ohhillno, billclintonisrapist, democratliesmatter, lyingcrookedhillary, hypocritehillary, crookedclintons, hillarylies, neverhilary, shelies, releasethetranscripts, stophillary2016, riskyhillary, hillaryliedpeopledied, corrupthillary, hillary4prison2016, nohillary2016, wehatehillary, whatmakeshillaryshortcircuit, crookedhillaryclinton, deletehillary, dropouthillary, lyinhillary, hillaryliesmatter, nevereverhillary, stophillary, neverhilllary, clintoncorruption, clintoncrime, notwithher, hillary2jail, imnotwithher, lockherup, corruptclintons, indicthillary, sickhillary, crookedhilary, crookedhillary, hillaryrottenclinton, theclintoncontamination, lyinghillary, clintoncollapse, clintoncrimefoundation, neverhillary, criminalhillary, crookedclinton, hillary4prison, killary, iwillneverstandwithher

1.4 A.4 Hashtags in favor of Trump (+, Trump)

trumppence2016, trumpstrong, donaldtrumpforpresident, rednationrising, deplorablesfortrump, makeamericaworkagain, latinos4trump, trumpiswithyou, blacks4trump, feelthetrump, votetrumppence2016, bikers4trump, votetrump2016, votetrumppence, americafirst, trumpcares, draintheswamp, votetrumpusa, trumppence16, gaysfortrump, buildthewall, trump2016, trumpwon, alwaystrump, onlytrump, maga3x, veteransfortrump, latinosfortrump, cafortrump, gays4trump, makeamericasafeagain, latinoswithtrump, trump16, woman4trump, womenfortrump, makeamericagreat, votegop, makeamericagreatagain, maga, trumptrain, gotrump, bikersfortrump, votetrumppence16, feminineamerica4trump, trumpwins, imwithhim, buildthatwall, babesfortrump, america1st, securetheborder, vets4trump, democrats4trump, women4trump, trumpforpresident, magax3, blacksfortrump, heswithus, presidenttrump, votetrump

B Evaluation on SemEval2016 Task 6 test-set

We evaluate our best performing CNN model on SemEval2016 Task 6 test-set with target ’Hillary Clinton’. This dataset contains 295 tweets with gold labels of ’AGAINST’, ’FAVOR’ or ’NEUTRAL’ as stance towards Hillary. Since our CNN is trained on ’FAVOR’ and ’AGAINST’ stance, following the same experimental setup, we extract tweets that are in favor ( or against the target (hillary clinton). We find that our model model performs well on the test-set with weighted F1-score of 0.75 (See Table 3 for the confusion matrix).

Table 3. Performance of CNN on ‘AGAINST’/‘FAVOR’ tweets in SemEval2016 Task 6 test-set (target ‘Hillary Clinton’)

C Topic list

money, japan, justice, climate change, economy, rapist, healthcare, podesta, obamacare, abortion, foreign, women, nato, cyber, russia, weapon, podesta email, voter fraud, benghazi, iran, assault, email, blm, gun, tape, job, podestaemail, middle east, police, p2, climatechange, 2ndamendment, amendment, audit, lgbt, 2nd amendment, appoint, climate, nafta, war, second amendment, black, middle class, mosul, tax, nuke, 2a, scotus, korea, isis, iraq, haiti, putin, trade, paytoplay, voterfraud, woman, china, law, nuclear, syria, secondamendment, rig, debt

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Khosla, S., Chhaya, N., Jindal, S., Saha, O., Srivastava, M. (2019). Do Events Change Opinions on Social Media? Studying the 2016 US Presidential Debates. In: , et al. Social Informatics. SocInfo 2019. Lecture Notes in Computer Science(), vol 11864. Springer, Cham. https://doi.org/10.1007/978-3-030-34971-4_20

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