BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//pretalx.com//spathum24//talk//8WTDGX BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-spathum24-8WTDGX@pretalx.com DTSTART;TZID=CET:20240927T113000 DTEND;TZID=CET:20240927T120000 DESCRIPTION:In the past decade\, there has been a growth of interest in exp loring the large amount of data present on social media\, generated by use rs around the world. Some calculations aim to confirm that are around 4.95 billion of users of social networks\, and they are generating huge quanti ties of information that can be used for research in geography and spatial humanities. Multiple investigations about data contained in social networ ks as Twitter \, Flickr\, Reddit\, TripAdvisor and other popular social ne tworks could be found in general academic research\, especially with in su bjects like Natural Language Processing (NLP) that also has been growing e xponentially in the past few years. With such a big source of information\ , there are a lot of work possibilities in different topics in which geogr aphy and spatial humanities must not be unconnected\, because as the avail able information is generated by people\, we can found a lot of different topics to study this kind of information. \n\nTrying to understand geograp hic data from social media information is one important goal for researche rs in recent years. But how can we obtain that geographic information from sources that are principally texts and pictures? In this research we try to answer that question\, in that context\, the main goal of this research is to make social media data a source from geographic information that co uld be used for several researching and decision making. \nThere has been some approaches to the main question by using social media data\, for exam ple\, some researches tried to use geotagged pictures to find some spatial patterns of sentiments with photos from Instagram and Flickr. Another app roach is using Named Entity Recognition (NER) to process TripAdvisor comme nts reviews using the text contents. In the case of twitter data\, there h as been 3 principal approaches to the matter: 1) Use the metadata of the i nformation (as they call geo-tagged tweets)\; 2) Inferring the geographic location of the tweet using a combination of metadata\, profile data and m aking predictions based on the language of the texts available in the cont ent being able to summarize a location of the origin of the tweet\, and fi nally\, 3) one of the most common approach by using techniques as NER.\n\n Except for a few cases of work with data from Indonesia\, China and India and focused to the local languages\, most of the work in this task has bee n in the English language or has used another approach like taking the wor ds from the original language and translating it to English with automated translation methods. Is in this context than a necessity of working with models that can be trained to use NER approaches in Spanish language speci fically for Spanish in Colombia has reached\, and to make the testing task with twitter data of Spanish tweets of Colombia could be useful to contri bute growing the NER tasks focused on identifying location in short texts as tweets. Furthermore\, NER tasks are too general to named entities\, so they are useful to find names\, location\, roles and organization\, in thi s case\, the main focus of this process is to use it focused in Locations. \n\nTo achieve that goal\, the exploration of NER methods has been taking place by exploring some supervised trained models for this task\, first\, testing some of the available as Stanford NER and Spacy library NER and co mparing it with the results of a trained supervised NER model using Colomb ian Spanish and Colombian toponyms. In this way we can see the improvement s of the NER tasks in the recognition of locations for this specific case. By comparing the methodological approaches\, and by generating the corres ponding models we could say this approach of a Colombian Language NER is a big contribution in several fields: 1) the researching in NER tasks of th e scientific community interested in NLP process and 2) the spatial humani ties\, geography community and institutions that can take another huge ge ographic information resource to further researching and decision making t owards the geo-spatial understanding on the world.\n\nAs this work is part of a bigger effort to understand the geographical space in Colombia with the use of data presented in texts (short texts in the case of twitter) pr ocessed with NLP\, testing NER tasks with Colombian Spanish to extract geo graphic locations is one of the first steps of the work\, so that is why t he future work will be related to use different approaches of unsupervised training as topic modeling and finally\, trying to summarize that extract ion with some topic and sentiment analysis in the tweets\, all of this in an effort to contribute to the spatial humanities and digital humanities a pproaches. DTSTAMP:20260508T142036Z LOCATION:MG1/02.05 SUMMARY:Extracting Geographic Information from Social Media Data\, an appro ach using NER with Colombian spanish - Brayan Oviedo URL:https://pretalx.com/spathum24/talk/8WTDGX/ END:VEVENT END:VCALENDAR