## 6 Conclusions  
Our work shows how the described method is able to follow the conversation in a community or context of interest, monitoring the labels (hashtags) that the related \"authorities\" adopt in their scope of activity online, and adjusting such tracking as their messages and topics vary, even if they do so in a dramatic manner. We also demonstrated how the method is robust when faced with potential disruptions while avoiding the need to compile an initial list of \"seed\" words that could either bias or underestimate the actual conversation taking place. Here, term selection does not depend on what is predicted by the researcher; instead, it is determined by the terms most used by the \"authorities\" together with the relative relevancy of each of them, a process that requires no external human intervention. The application of this method could, therefore, enrich any work that seeks to track the conversation around a topic without manual intervention. This approach reduces bias as well as the manual effort required when tracking a particular event--such as a debate--or other situation. The framework provided is applicable not only to politics but also to any other context, and therefore, its implications for research on social media, in general, are vast. Additionally, it must be noted that this mechanism is not limited to Twitter; with the proper adaptation, it could be applied to other SNS with equivalent characteristics. In summary:  
* The proposed methodology is able to follow the conversation around a topic and optimize its tracking, with a quick adaptation and no need for human supervision;
* The methodology is also robust against undesired events, misspellings, and the use of different languages and alternative terms;
* Fixed hashtag selection limits the information that researchers might be able to extract; it is desirable to adopt a flexible and dynamic approach. A few restrictions that limit this methodology should be noted. The first one is that an accurate and expert selection of the authorities is required. The researcher must, therefore, identify the relevant users in the discussion with care. In the case of politics, which was explored in our case study, selection is arguably straightforward as politicians, candidates, and political parties clearly lead the discourse around such matters. Other contexts, however, might have less evident leaders of opinion. On the other hand, there are a few parameters that give flexibility to the system (length of the time window, weight, stop words, number of topics, etc. ), but this is a double-edged sword as their determination is critical for the proper functioning of the tracking. Therefore, testing is suggested before committing to a full-scale extraction, either in other smaller cases or in comparable cases when the uniqueness of the event does not allow for testing in the exact same context. Dynamically adapting the list of authorities (adding or removing users from that set as things change over time) will be considered in future work, as especially in long-term tracking, some users might lose relevancy (e.g., candidates that have not been elected or have resigned), while others not included in the initial set might rise in relevancy (e.g., influential third parties or surprise elements). Identifying the most relevant users in a particular period [40] could mean adding or removing members from the list. Additionally, and as appropriate parameter setting is critical to generalize the applicability of the model, we will explore how these parameters can be assessed and their results compared in order to optimize their values in a minimally invasive way. Conceptualization, M.M.-C. and S.S.-A. ; Formal analysis, M.M.-C.; Investigation, M.M.-C.; Methodology, M.M.-C. and S.S.-A. ; Software, M.M.-C.; Supervision, S.S.-A., E.G.-B. and M.-A.S.; Validation, S.S.-A., E.G.-B. and M.-A.S.; Visualization, M.M.-C.; Writing--original draft, M.M.-C.; Writing--review & editing, S.S.-A., E.G.-B.