Validación de la propuesta

More and more people possess smart phones and other advances devices provided with

technologies to produce and collect visual content. In the next coming years, it is expected that visual content will increase in number and that image-based social media platforms and applications will grow in number and use. Therefore, it is important to gather more insight in the predictive

possibilities of visual sentiment. This research adds to the current knowledge with regard to whether an image-based social media platform like Instagram is different in its predictive ability than an text- based social media platform like Twitter. There remain enormous differences between for example the motives of using the social media platforms and the expressions of users. This research adds to the current understanding of the predictive value of Instagram, which is at the moment only ill- defined. The ongoing increasing number of Instagram users and the high influence during purchasing decisions of Instagram on users further demands for research with regard to the predictive value of Instagram. This research shows the potential of Instagram as an source for successfully predicting a phenomenon of interest. In the next coming years, not only the use of Instagram will increase, but also other image-based platforms will increase in use. Applications like Snapchat are popular, but it might be expected that the number of applications with visual possibilities will increase in the coming years.

But what do these results mean for the movie industry? First, it reveals the predictive potential of Twitter and especially Instagram in predicting movie box office revenues. More and more the internet is used for information seeking about movies and especially social media applications are used because information can be easily and quickly gathered. So therefore it is important to know where the box office revenues come from. This helps the movie industry to better target potential visitors, to better distribute marketing budgets over different (social media) channels and to invest more in pre-release promotional activities if it seems that a movie might underperform due to for example unfamiliarity by the crowd. Furthermore, this research is also interesting in terms of logistics and planning, because if a cinema knows what high or low values on a certain variable mean, this would enable them to accurately plan personnel, assigning rooms to the movie which fit with the number of visitors, the purchase of snacks and drinks, and other logistic issues. Besides for the movie distributors, it is also important for the cinemas to know where to spend their advertising budget on in order to attract movie visitors. Especially when people could be potentially interested in a movie, but are still in doubt whether to visit the movie. Furthermore, it is also important to know for investors and movie producers how comparable movies behave on social media with regard to post

74 volumes and sentiment, because this helps them to better decide upon investing in for example new movie productions. Furthermore, they can also compare whether they are satisfied with the output in terms of movie box office revenue levels when investing certain sums of money on social media advertising or that the investment should be increased in order to gain higher movie box office revenues.

In addition, this research also contributes to the applicability of the Complura tool for predictive purposes. The Complura tool shows that it is able to produce visual sentiment scores suitable for research in different settings. Further, the generalizability of research towards other disciplines of social media is also important. This research builds on previous research with regard to volume- related and sentiment-related variables predicting movie box office revenues. This study found similar relationships between Twitter and movie box office revenues and extends the research field with knowledge with regard to Instagram. Particularly valuable are the results that in some cases visual sentiment is a significant and strong predictor. The findings revealed that audience age and genre matter regarding visual sentiment. This research could be extended to both text-based and/or image-based driven social media networking sites. Important issues that should be tackled are privacy issues regarding data for the research, which will come more in play due to the actual debates about data privacy. Nevertheless, it is interesting to find out whether the same results with regard to volume-related and sentiment-related variables can be observed on other platforms and whether different features on social media networking sites impact the outcomes.

Additionally, concepts of social media strategies are currently explored (Spil, Effing, & Both, 2016). A well-defined social media strategies consists of various elements. One of these elements is

monitoring, but unless its importance companies seek to monitor until the later stages of

implementing a social media strategy. This results into a lack with regard to social media monitoring. Hopefully the insights of this research could show social media managers that monitoring social media volume could provide essential real-time updates and that social media monitoring can be used as a powerful tool for prediction. Prediction models based on Instagram and Twitter empowers social media managers to monitor (the progress with regard to reaching) targets.

Possibly the most interesting field for predictive social media research will be the development of predictive models that are able to process real-time data from different social media network sites. Technological developments and advances in machine learning enables us to reach high degrees of prediction accuracy. Prediction algorithms can be improved in order to more accurately predict a phenomenon of interest. Also with regard to Big Data as an upcoming trend, this research could be seen as first steps in order to improve the predictive value out of large data sets. Optimized

75 algorithms could enable better analysis of Big Data, which extends the possibilities with regard to output. Fine tuning specific features and parameters in machine learning algorithms will be an challenge in the future.

Also in other settings the findings of this research are relevant. Besides for companies, this research is also relevant for other fields like for example politics. A first step in understanding the predictive value of Instagram with regard to politics has been made by Schmidbauer et al. (2018), however, research in this field is quite limited. This research extends the current knowledge with regard to Instagram and Twitter by showing the predictive impact of volume-related and sentiment-related variables on a phenomenon of interest. More specifically, identifying significant variables as

predictors could be relevant for politicians to improve their campaign by for example knowing when and how to invest their advertising budgets in order to reach the optimal effect and on which social media networking should be focused in order to influence public opinion in a beneficial way. Further, it also helps politicians to monitor how they are performing and to identify popular and/or relevant topics discussed by the crowd. This helps the politician to better understand what should be done and discussed in debates in order to receive support from citizens.