nach oben

Knowledge and Information Systems

Erschienen in:

19.10.2019 | Regular Paper

Recurrent random forest for the assessment of popularity in social media

2016 US election as a case study

verfasst von: Farideh Tavazoee, Claudio Conversano, Francesco Mola

Erschienen in: Knowledge and Information Systems | Ausgabe 5/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Popularity in social media is mostly interpreted by drawing a relationship between a social media account and its followers. Although understanding popularity from social media has been explored for about a decade, to our knowledge, the extent to which the account owners put efforts to enhance their popularity has not been evaluated in detail. In this paper, we focus on Twitter, a popular social media, and consider the case study of the 2016 US elections. More specifically, we aim to assess whether candidates endeavor to improve their style of tweeting over time to be more attractive to their followers. An ad hoc-defined predictive model based on a recurrent random forest is used for this purpose. To this end, we build a classification model whose features are obtained from the characteristics of a set of content/sentiment information extracted from the tweets. Next, we derive an index of social media popularity for both candidates. Results show that Trump wisely exploited Twitter to attract more people by tweeting in a well-organized and desirable manner and that his tweeting style has increased his popularity in social media. The differences in the tweeting styles of the two presidential candidates and the links between the sentiments arising from candidates’ tweets and their popularity index are also investigated.

Vorheriger Artikel Structured query construction via knowledge graph embedding

Nächster Artikel An effective few-shot learning approach via location-dependent partial differential equation

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

http://www.alexa.com/topsites.

http://www.pewresearch.org/fact-tank/2014/11/12/facebook-and-twitter-as-political-forums-two-different-dynamics/.

http://www.cbsnews.com/news/president-elect-trump-says-social-media-played-a-key-role-in-his-victory/.

http://www.foxnews.com/tech/2017/05/21/twitter-co-founder-apologizes-for-helping-elect-trump.html.

Tweets were limited to 140 characters during the election, but Twitter has doubled that to 280 characters since September 2017.

https://blog.twitter.com/2016/how-election2016-was-tweeted-so-far.

www.readwritethink.org/files/resources/lesson_images/lesson209/definition_style.pdf.

https://github.com/Jefferson-Henrique/GetOldTweets-python.

www.smh.com.au/world/north-america/donald-trump-recorded-having-extremely-lewd-conversation-about-women-20161008-grxrwp.html

https://www.smh.com.au/world/north-america/donald-trump-recorded-having-extremely-lewd-conversation-about-women-20161008-grxrwp.html.

Asur S, Huberman BA (2010) Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology—volume 01, WI-IAT ’10. IEEE Computer Society, Washington, DC, USA, pp 492–499. https://doi.org/10.1109/WI-IAT.2010.63

Bandari R, Asur S, Huberman BA (2012) The pulse of news in social media: Forecasting popularity. CoRR abs/1202.0332. https://dblp.org/rec/html/journals/corr/abs-1202-0332. Accessed 14 Oct 2019

Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistics: posters, COLING ’10. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 36–44. http://dl.acm.org/citation.cfm?id=1944566.1944571

Bradshaw S, Howard PN (2017) Troops, trolls and troublemakers: a global inventory of organized social media manipulation. http://comprop.oii.ox.ac.uk/wp-content/uploads/sites/89/2017/07/Troops-Trolls-and-Troublemakers.pdf. Working paper available at http://comprop.oii.ox.ac.uk/

Brady WJ, Wills JA, Jost JT, Tucker JA, Van Bavel JJ (2017) Emotion shapes the diffusion of moralized content in social networks. Proc Natl Acad Sci 114(28):7313–7318. https://doi.org/10.1073/pnas.1618923114 CrossRef

Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324 CrossRefMATH

Briem GJ, Benediktsson JA, Sveinsson JR (2002) Multiple classifiers applied to multisource remote sensing data. IEEE Trans Geosci Remote Sens 40(10):2291–2299CrossRef

Burnap P, Gibson R, Sloan L, Southern R, Williams ML (2015) 140 characters to victory? Using twitter to predict the UK 2015 general election. CoRR abs/1505.01511. http://arxiv.org/abs/1505.01511

Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th international conference on machine learning, ICML ’08. ACM, New York, NY, USA, pp 96–103. https://doi.org/10.1145/1390156.1390169

10.

Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on machine learning, ICML ’06. ACM, New York, NY, USA, pp 161–168. https://doi.org/10.1145/1143844.1143865

11.

Chan JCW, Paelinckx D (2008) Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ 112(6):2999–3011. https://doi.org/10.1016/j.rse.2008.02.011 CrossRef

12.

Chen J, Li K, Tang Z, Bilal K, Yu S, Weng C, Li K (2017) A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Trans Parallel Distrib Syst 28(4):919–933. https://doi.org/10.1109/TPDS.2016.2603511 CrossRef

13.

Cleveland WS (1981) Lowess: a program for smoothing scatterplots by robust locally weighted regression. Am Stat 35(1):54–54CrossRef

14.

Darwish K, Magdy W, Zanouda T (2017) Trump vs. Hillary: what went viral during the 2016 US presidential election. In: Ciampaglia GL, Mashhadi A, Yasseri T (eds) Social Informatics, Springer International Publishing, Cham, pp 143–161. https://doi.org/10.1007/978-3-319-67217-5_10

15.

Dixon MF (2018) Sequence classification of the limit order book using recurrent neural networks. J Comput Sci 24:277–286. https://doi.org/10.1016/j.jocs.2017.08.018

16.

Eysenbach G (2011) Can tweets predict citations? Metrics of social impact based on twitter and correlation with traditional metrics of scientific impact. J Med Internet Res 13(4):e123. https://doi.org/10.2196/jmir.2012 CrossRef

17.

Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181MathSciNetMATH

18.

Gayo-Avello D (2011) Don’t turn social media into another ‘literary digest’ poll. Commun ACM 54(10):121–128. https://doi.org/10.1145/2001269.2001297 CrossRef

19.

Gehrke J, Ramakrishnan R, Ganti V (2000) Rainforest—a framework for fast decision tree construction of large datasets. Data Min Knowl Discov 4(2):127–162. https://doi.org/10.1023/A:1009839829793 CrossRef

20.

González-Ibáñez R, Muresan S, Wacholder N (2011) Identifying sarcasm in twitter: a closer look. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies: short papers—volume 2, HLT ’11. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 581–586. http://dl.acm.org/citation.cfm?id=2002736.2002850

21.

Gou C, Shen H, Du P, Wu D, Liu Y, Cheng X (2018) Learning sequential features for cascade outbreak prediction. Knowl Inf Syst. https://doi.org/10.1007/s10115-017-1143-0 CrossRef

22.

Hastie T (2018) GAM: generalized additive models. https://CRAN.R-project.org/package=gam. R package version 1.16

23.

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer, BerlinCrossRef

24.

Hong L, Dan O, Davison BD (2011) Predicting popular messages in twitter. In: Proceedings of the 20th international conference companion on world wide web, WWW ’11. ACM, New York, NY, USA, pp 57–58. https://doi.org/10.1145/1963192.1963222

25.

Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’04. ACM, New York, NY, USA, pp 168–177. https://doi.org/10.1145/1014052.1014073

26.

Jahanbakhsh K, Moon Y (2014) The predictive power of social media: on the predictability of U.S. presidential elections using twitter. CoRR abs/1407.0622. http://arxiv.org/abs/1407.0622

27.

Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies—volume 1, HLT ’11. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 151–160. http://dl.acm.org/citation.cfm?id=2002472.2002492

28.

Jockers ML (2015) Syuzhet: extract sentiment and plot arcs from text. https://github.com/mjockers/syuzhet

29.

Jungherr A, Jürgens P, Schoen H (2012) Why the pirate party won the German election of 2009 or the trouble with predictions: a response to tumasjan, a., sprenger, t. o., sander, p. g., amp; welpe, i. m. “predicting elections with twitter: what 140 characters reveal about political sentiment”. Soc Sci Comput Rev 30(2):229–234. https://doi.org/10.1177/0894439311404119 CrossRef

30.

Lerman K, Hogg T (2010) Using a model of social dynamics to predict popularity of news. In: Proceedings of the 19th international conference on world wide web, WWW ’10. ACM, New York, NY, USA, pp 621–630. https://doi.org/10.1145/1772690.1772754

31.

Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2(3):18–22

32.

Mohammad SM, Turney PD (2013) Crowdsourcing a word-emotion association lexicon. Comput Intell 29(3):436–465MathSciNetCrossRef

33.

Nielsen FÅ (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs. CoRR abs/1103.2903. http://arxiv.org/abs/1103.2903

34.

O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the fourth international conference on weblogs and social media, ICWSM 2010, Washington, DC, USA, May 23–26

35.

Oliver JE, Rahn WM (2016) Rise of the trumpenvolk. Ann Am Acad Polit Soc Sci 667(1):189–206. https://doi.org/10.1177/0002716216662639 CrossRef

36.

Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Calzolari N, Choukri K, Maegaard B, Mariani J, Odijk J, Piperidis S, Rosner M, Tapias D (eds) Proceedings of the seventh international conference on language resources and evaluation (LREC’10). European Language Resources Association (ELRA), Valletta, Malta

37.

Pal M, Mather PM (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86(4):554–565. https://doi.org/10.1016/S0034-4257(03)00132-9 CrossRef

38.

Palanisamy P, Yadav V, Elchuri H (2013) Serendio: simple and practical lexicon based approach to sentiment analysis. https://www.aclweb.org/anthology/S13-2091/. Accessed 15 Oct 2019

39.

Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135. https://doi.org/10.1561/1500000011 CrossRef

40.

Petrovic S, Osborne M, Lavrenko V (2011) Rt to win! Predicting message propagation in twitter. In: Adamic LA, Baeza-Yates RA, Counts S (eds) ICWSM

41.

R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

42.

Sang ETK, Bos J (2012) Predicting the 2011 Dutch senate election results with twitter. In: Proceedings of the workshop on semantic analysis in social media. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 53–60. http://dl.acm.org/citation.cfm?id=2389969.2389976

43.

Simonoff JS (1996) Smoothing methods in statistics. Springer, New YorkCrossRef

44.

Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: Proceedings of the 2010 IEEE second international conference on social computing, SOCIALCOM ’10. IEEE Computer Society, Washington, DC, USA, pp 177–184. https://doi.org/10.1109/SocialCom.2010.33

45.

Tavazoee F, Conversano C, Mola F (2017) Investigating the relationship between tweeting style and popularity: the case of US presidential election 2016. Springer, Cham, pp 112–123. https://doi.org/10.1007/978-3-319-69548-8_9 CrossRef

46.

Tumasjan A, Sprenger T, Sandner P, Welpe I (2010) Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Proceedings of the fourth international AAAI conference on weblogs and social media, pp 178–185

47.

Vadim K, Stevens A, Subrahmanian VS (2015) Using twitter sentiment to forecast the 2013 Pakistani election and the 2014 Indian election. IEEE Intell Syst 30(1):2–5. https://doi.org/10.1109/MIS.2015.16 CrossRef

48.

Wang H, Can D, Kazemzadeh A, Bar F, Narayanan S (2012) A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle. In: Proceedings of the ACL 2012 system demonstrations, ACL ’12. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 115–120. http://dl.acm.org/citation.cfm?id=2390470.2390490

49.

Wang XF (2010) fANCOVA: nonparametric analysis of covariance. https://CRAN.R-project.org/package=fANCOVA. R package version 0.5-1

50.

Wu B, Shen H (2015) Analyzing and predicting news popularity on twitter. Int J Inf Manag 35(6):702–711. https://doi.org/10.1016/j.ijinfomgt.2015.07.003 CrossRef

51.

Zaman T, Fox EB, Bradlow ET (2013) A Bayesian approach for predicting the popularity of tweets. Ann Appl Stat 8, no. 3, 1583–1611. https://doi.org/10.1214/14-AOAS741. https://projecteuclid.org/euclid.aoas/1414091226

52.

Zhao Q, Erdogdu MA, He HY, Rajaraman A, Leskovec J (2015) SEISMIC: a self-exciting point process model for predicting tweet popularity. In: KDD ‘15: proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining, Sydney, NSW, Australia. ACM, New York, pp 1513–1522. https://doi.org/10.1145/2783258.2783401

Titel: Recurrent random forest for the assessment of popularity in social media
2016 US election as a case study
verfasst von: Farideh Tavazoee
Claudio Conversano
Francesco Mola
Publikationsdatum: 19.10.2019
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 5/2020
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-019-01410-w

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 5/2020

CDLFM: cross-domain recommendation for cold-start users via latent feature mapping

An FPA and GA-based hybrid evolutionary algorithm for analyzing clusters

An effective few-shot learning approach via location-dependent partial differential equation

Memory-based random walk for multi-query local community detection

Structured query construction via knowledge graph embedding

From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in Belgium