## 5 Conclusions  
Several conclusions can be drawn from the exploratory data analysis presented in this paper. We launched this first analysis through all the information contained in our database, containing different cities and countries from all over the world. The first conclusion is that the predominant language of the reports is English, and therefore the text analysis must be carried out in English to obtain the most out of the database. The second conclusion is that there is a low rate of reports made by witnesses, which shows that there is a need to make people more aware of the importance of intervening and reporting incidents. Intervention of a witness protects the feelings of the victim and contributes to the Hollaback! project for reducing harassment and violence. Another conclusion that can be drawn is that there is an uneven distribution of reports around the world, which may suggest the need to expand the reach of the platform to places where it is not known. Stronger emphasis on guarantees of anonymity should help to break down cultural barriers and lead to more reports being made around the world. In the machine learning process focused on the city of Madrid and selecting English as the language, it can be concluded that, related to the text processing tools, more advanced feature engineering techniques could be applied [35]. As with many other research works, we appreciated the importance of the feature extraction process for attaining a proper performance of the machine learning models. The results in our target class (\"with witness\") considerably improved when we amended the feature selection process. Thus, unsupervised learning NLP techniques such as Word2 Vec would be extremely useful for vocabulary augmentation based on semantic proximity and to find more selective clusters of features characterizing the descriptions with bystanders. Multiple conclusions can be drawn from the binary classification algorithms. Regarding the different machine learning models, the behavior was very similar among all of them, except for SGD. As described in Section 4, the approach with an exhaustive feature selection had better results, reinforcing the importance of a good feature extraction in the text, not only during training but also before processing the test data. With this approach, we reached an accuracy of 68%, a precision of 67% and a sensitivity of 89% in the target class (descriptions with witnesses). Those metrics could also increase even further when augmenting the training dataset. Thus, this model could be used as a first automatic approach to filter out descriptions in which bystanders were present. Figure 20: Simple model metrics applied to full descriptions. **Author Contributions:** Conceptualization, M.A.-P. and A.L. ; methodology, C.P. and R.P. ; software, M.A.-P. and R.P. ; validation, A.L. and C.P. ; investigation, A.L., C.P. and M.A.-P.; writing--original draft preparation, C.P., M.A.-P., A.L. and R.P. ; writing--review and editing, A.L. and R.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not Applicable. **Informed Consent Statement:** Not Applicable. **Data Availability Statement:** All data were collected from: [[https://www.ihollaback.org/resources/](https://www.ihollaback.org/resources/)]([https://www.ihollaback.org/resources/](https://www.ihollaback.org/resources/)) (accessed on 28 May 2021). **Conflicts of Interest:** The authors declare no conflict of interest. * Wordcloud (2021) Wordcloud 1.7.0. Available online: [[https://pypi.org/project/wordcloud/](https://pypi.org/project/wordcloud/)]([https://pypi.org/project/wordcloud/](https://pypi.org/project/wordcloud/)) (accessed on 28 May 2021). * Salton and McGill (1983) [PERSON]; [PERSON] _Introduction to Modern Information Retrieval_; McGraw-Hill: New York, NY, USA, 1983. * [PERSON] et al. (1998) [PERSON]; [PERSON]; [PERSON] An Introduction to Latent Semantic Analysis.