CRITERIOS DE ELIMINACIÓN.

Appendix 3 – Movie box office revenue and social media predictions literature

Appendix 4 – Instagram

Appendix 5 – Twitter

Appendix 6 – Comparison of Instagram and Twitter

Appendix 7 – Movies and release dates

Appendix 8 – Weekly trajectories of volume-related Instagram variables

Appendix 9 – Weekly trajectories of volume-related Twitter variables

Appendix 10 – Weekly movie box office revenues

Appendix 11 – Correlation analysis

Appendix 12 – Additional analyses

102

Appendix 1 – Overview of literature with regard to social media predictor variables

103 Literature:

Asur, S., & Huberman, B. A. (2010). Predicting the Future with Social Media. 2010 IEEE/WIC/ACM International Conference on Web Intelligence and

Intelligent Agent Technology, 1, 492–499. https://doi.org/10.1109/WI-IAT.2010.63

Baek, H., Ahn, J., & Oh, S. (2014). Impact of tweets on box office revenue: Focusing on when tweets are written. ETRI Journal, 36(4), 581–590. https://doi.org/10.4218/etrij.14.0113.0732

Bhavsar, P., Kumar, A., Kumar, S., Gaur, A., & Sheikh, S. G. (2017). Outcome and Prediction of Popularity of Motion Picture Using Social Media, 3245–3248. Broekhuizen, T. L. J., Delre, S. A., & Torres, A. (2011). Simulating the cinema market: How cross-cultural differences in social influence explain box office

distributions. Journal of Product Innovation Management, 28(2), 204–217. https://doi.org/10.1111/j.1540-5885.2011.00792.x

Dellarocas, C., Zhang, X., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of

Interactive Marketing, 21(4), 23–45. https://doi.org/10.1002/dir.20087

Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. (2017). The power of the “like” button: The impact of social media on box office. Decision Support Systems, 94, 77– 84. https://doi.org/10.1016/j.dss.2016.11.002

Divakaran, P. K. P., Palmer, A., Søndergaard, H. A., & Matkovskyy, R. (2017). Pre-launch Prediction of Market Performance for Short Lifecycle Products Using Online Community Data. Journal of Interactive Marketing, 38, 12–28. https://doi.org/10.1016/j.intmar.2016.10.004

Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with Web search. Proceedings of the National

Academy of Sciences of the United States of America, 107(41), 17486–90. https://doi.org/10.1073/pnas.1005962107

Hennig-Thurau, T., Marchand, A., & Hiller, B. (2012). The relationship between reviewer judgments and motion picture success: Re-analysis and extension.

Journal of Cultural Economics, 36(3), 249–283. https://doi.org/10.1007/s10824-012-9172-8

Ho, J. Y. C., Dhar, T., & Weinberg, C. B. (2009). Playoff payoff: Super Bowl advertising for movies. International Journal of Research in Marketing, 26(3), 168– 179. https://doi.org/10.1016/j.ijresmar.2009.06.001

Houston, M. B., Kupfer, A. K., Hennig-Thurau, T., & Spann, M. (2018). Pre-release consumer buzz. Journal of the Academy of Marketing Science, 1–23. https://doi.org/10.1007/s11747-017-0572-3

Jashinsky, J., Burton, S. H., Hanson, C. L., West, J., Giraud-Carrier, C., Barnes, M. D., & Argyle, T. (2014). Tracking suicide risk factors through Twitter in the US. Crisis, 35(1), 51–59. https://doi.org/10.1027/0227-5910/a000234

104 Jin, X., Gallagher, A., Cao, L., Luo, J., & Han, J. (2010). The wisdom of social multimedia: using Flickr for prediction and forecast. Proceedings of the

International Conference on Multimedia, 1235–1244. https://doi.org/10.1145/1873951.1874196

Jungherr, A. (2013). Tweets and votes, a special relationship. Proceedings of the 2nd Workshop on Politics, Elections and Data - PLEAD ’13, 5–14. https://doi.org/10.1145/2508436.2508437

Karniouchina, E. V. (2011). Impact of star and movie buzz on motion picture distribution and box office revenue. International Journal of Research in

Marketing, 28(1), 62–74. https://doi.org/10.1016/j.ijresmar.2010.10.001

Kim, Cha, & Kim. (2016). Targeted Ads Experiment on Instagram. In Social informatics : 8th International Conference, SocInfo 2016 (Vol. 1, p. 545). https://doi.org/10.1007/978-3-319-47874-6

Kim, Hong, & Kang. (2017). Box Office Forecasting considering Competitive Environment and Word-of-Mouth in Social Networks: A Case Study of Korean Film Market. Computational Intelligence and Neuroscience, 2017. https://doi.org/10.1155/2017/4315419

Lee, Keeling, & Urbaczewski. (2017). The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales. Information Systems

Frontiers, 1–16. https://doi.org/10.1007/s10796-017-9778-7

Lipizzi, C., Iandoli, L., & Marquez, J. E. R. (2016). Combining structure, content and meaning in online social networks: The analysis of public’s early reaction in social media to newly launched movies. Technological Forecasting and Social Change, 109, 35–49. https://doi.org/10.1016/j.techfore.2016.05.013 Liu, T., Ding, X., Chen, Y., Chen, H., & Guo, M. (2016). Predicting movie Box-office revenues by exploiting large-scale social media content. Multimedia Tools

and Applications, 75(3), 1509–1528. https://doi.org/10.1007/s11042-014-2270-1

Liu, Y. (2006). Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of Marketing, 74(1), 1–49. https://doi.org/10.1509/jmkg.70.3.74

Mestyán, M., Yasseri, T., & Kertész, J. (2013). Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data. PLoS ONE, 8(8). https://doi.org/10.1371/journal.pone.0071226

Oghina, A., Breuss, M., Tsagkias, M., & De Rijke, M. (2012). Predicting IMDB movie ratings using social media. Lecture Notes in Computer Science (Including

Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7224 LNCS, 503–507. https://doi.org/10.1007/978-3-642-28997-

2_51

Qin, L. (2011). Word-of-Blog for Movies: a Predictor and an Outcome of Box Office Revenue? Journal of Electronic Commerce Research, 12(3), 187–198. Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? the effect of tweets on movie sales. Decision Support Systems, 55(4), 863–870.

105 Schmidbauer, H., Rösch, A., & Stieler, F. (2018). The 2016 US presidential election and media on Instagram: Who was in the lead? Computers in Human

Behavior, 81, 148–160. https://doi.org/10.1016/j.chb.2017.11.021

Young, S. D., Rivers, C., & Lewis, B. (2014). Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes.

106 Sentiment-related variables

107 Literature:

Ahsan, U., De Choudhury, M., & Essa, I. (2017). Towards using visual attributes to infer image sentiment of social events. Proceedings of the International

Joint Conference on Neural Networks, 2017–May(April 2016), 1372–1379. https://doi.org/10.1109/IJCNN.2017.7966013

Apala, K. R., Jose, M., Motnam, S., Chan, C.-C., Liszka, K. J., & De Gregorio, F. (2013). Prediction of movies box office performance using social media.

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, (July), 1209–1214.

https://doi.org/10.1145/2492517.2500232

Asur, S., & Huberman, B. A. (2010). Predicting the Future with Social Media. 2010 IEEE/WIC/ACM International Conference on Web Intelligence and

Intelligent Agent Technology, 1, 492–499. https://doi.org/10.1109/WI-IAT.2010.63

Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013). Large-scale visual sentiment ontology and detectors using adjective noun pairs. Proceedings of the

21st ACM International Conference on Multimedia - MM ’13, 223–232. https://doi.org/10.1145/2502081.2502282

Cai, & Xia. (2015). Convolutional Neural Networks for Multimedia Sentiment Analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes

in Artificial Intelligence and Lecture Notes in Bioinformatics), 9362, 159–167. https://doi.org/10.1007/978-3-319-25207-0

Flaes, B., Rudinac, & Worring. (2016). What Multimedia Sentiment Analysis Says About City Liveability. Lecture Notes in Computer Science (Including

Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9626, 824–829. https://doi.org/10.1007/978-3-319-30671-1

Gelli, F., Uricchio, T., Bertini, M., Del Bimbo, A., & Chang, S.-F. (2015). Image Popularity Prediction in Social Media Using Sentiment and Context Features.

Proceedings of the 23rd ACM International Conference on Multimedia - MM ’15, 907–910. https://doi.org/10.1145/2733373.2806361

Graesser, L., Gupta, A., Sharma, L., & Bakhturina, E. (2017). Sentiment Classification using Images and Label Embeddings. Retrieved from http://arxiv.org/abs/1712.00725

Huber, B., Mcduff, D., Brockett, C., Galley, M., & Dolan, B. (2018). Emotional Dialogue Generation using Image-Grounded Language Models. https://doi.org/10.1145/3173574.3173851

Islam, J., & Zhang, Y. (2016). Visual Sentiment Analysis for Social Images Using Transfer Learning Approach. 2016 IEEE International Conferences on Big Data

and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud- SocialCom-SustainCom), 124–130. https://doi.org/10.1109/BDCloud-SocialCom-SustainCom.2016.29

Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602–611. https://doi.org/10.1007/s11704-016-5453-2

108 Jou, B., Chen, T., Pappas, N., Redi, M., Topkara, M., & Chang, S.-F. (2015). Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment

Ontology, (1). https://doi.org/10.1145/2733373.2806246

Lipizzi, C., Iandoli, L., & Marquez, J. E. R. (2016). Combining structure, content and meaning in online social networks: The analysis of public’s early reaction in social media to newly launched movies. Technological Forecasting and Social Change, 109, 35–49. https://doi.org/10.1016/j.techfore.2016.05.013 Liu, T., Ding, X., Chen, Y., Chen, H., & Guo, M. (2016). Predicting movie Box-office revenues by exploiting large-scale social media content. Multimedia Tools

and Applications, 75(3), 1509–1528. https://doi.org/10.1007/s11042-014-2270-1

Pappas, N., Redi, M., Topkara, M., Jou, B., Liu, H., Chen, T., & Chang, S.-F. (2016). Multilingual Visual Sentiment Concept Matching. https://doi.org/10.1145/2911996.2912016

Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? the effect of tweets on movie sales. Decision Support Systems, 55(4), 863–870. https://doi.org/10.1016/j.dss.2012.12.022

Schumaker, R. P., Jarmoszko, A. T., & Labedz, C. S. (2016). Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decision

Support Systems, 88, 76–84. https://doi.org/10.1016/j.dss.2016.05.010

Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S. F., & Pantic, M. (2017). A survey of multimodal sentiment analysis. Image and Vision Computing, 65, 3–14. https://doi.org/10.1016/j.imavis.2017.08.003

White. (2016). Forecasting Canadian Elections Using Twitter. Advances in Artificial Intelligence, 9673, 1–15. https://doi.org/10.1007/978-3-319-34111-8 You, Q., Luo, J., Jin, H., & Yang, J. (2016). Cross-modality Consistent Regression for Joint Visual-Textual Sentiment Analysis of Social Multimedia. Proceedings

109