## 2 Background
### Topic Tracking  
Previous work has pointed out the difficulties of implementing reliable, precise, and fast trend detection [16]. The high volume of information, the myriad different topics under discussion in any particular time, and the significant variations in the time and volume scales of social datasets stand in the way of direct tracking. Many authors have addressed the problem of detecting new events from a stream [17; 18]. The work by [PERSON] et al. [19] described a method applicable to a stream of Twitter posts, but although their system outperformed previous approaches, the results invariably include spurious information and not only news events as one would expect. One way to overcome the limitations related to the analysis and classification of corpora is to look at the Twitter hashtags (keywords or terms starting with \"#\"), which are the most common feature for users to connect and relate to within a larger networked discourse [20]. [PERSON] et al. have shown how messages that include such hashtags have, in general, more informational value than tweets without them; these labeled messages are also often longer, connected with other topics, and enriched with hyperlinks [21]. Additionally, as [PERSON] and [PERSON] [22] note, using hashtags is not an individual action: many actors, such as politicians, make use of them to reach outside their own connections and contacts. This is an example of how the use of social media by many sectors is closely-related to their professional practice. To be able to track hashtags accurately is, thus, critical to be able to analyze the discourse in Twitter. However, difficulties arise when the researcher needs to predict what hashtag or word is going to be used by the public in a particular situation, and whether it is going to change in the process, alternating with modified or customized hashtags, so a flexible approach to the tracking becomes a requirement. In spite of this, many previous works follow a static tracking approach. For instance, [PERSON] and [PERSON] [23] used Twitter to monitor the discussion around a constitutional referendum held in Italy for five weeks. Even with a potentially disputed debate, they opted for a manual selection of five hashtags. Similarly, [PERSON] et al. [24] tracked a single hashtag, #WorldEnvironmentDay, to assess the public opinion around such an event. However, such opinion included only users that used hashtags in English, and therefore, discarded variants or hashtags written in different languages that could, potentially, be even more widely used, individually or altogether, than the official tag. [PERSON] et al. [25] also used four static hashtags to analyze the messages in Twitter in a natural disaster, even when the typhoon went on for five days. In their work about the EU 2014 election trends, [PERSON] et al. [26] admit that when the selection of hashtags is static, missing data and losing track of the conversation is inevitable, even when they \"aggregated tweets written in the respective language that contained a party's name, its abbreviation, its Twitter account name and some possible misspells\" and \"excluded several ambiguous keywords in an attempt to reduce the noise\". These examples reflect how when only the expected generic terms or the mainstream hashtags are used, relevant information is lost as they leave little space to unexpected events, shortly lived relevant topics or unusual wordings. In this line, it is also worth noting how different languages, abbreviations, misspellings, or ambiguous keywords can be problematic when using a static approach. Within the general analysis of trending topics on Twitter, some authors have tried to analyze what makes a topic trend [27], what characterizes these topics [28], what different types of emerging topics exist [29], and even what characteristics are shared by users who started or had a greater influence in the dissemination of trending topics--what has been referred as Twitter trend demographics [30].