Changes in T witter geolocations: Insights and suggestions for future usage (2021)

Abstract: Twitter data has become established as a valuable source of data for various application scenarios in the past years. For many such applications, it is necessary to know where Twitter posts (tweets) were sent from or what location they refer to. Researchers have frequently used exact coordinates provided in a small percentage of tweets, but Twitter removed the option to share these coordinates in mid-2019. Moreover, there is reason to suspect that a large share of the provided coordinates did not correspond to GPS coordinates of the user even before that. In this paper, we explain the situation and the 2019 policy change and shed light on the various options of still obtaining location information from tweets. We provide usage statistics including changes over time, and analyze what the removal of exact coordinates means for various common research tasks performed with Twitter data. Finally, we make suggestions for future research requiring geolocated tweets.

## introduction twitter data has become an invaluable source of information for a range of application scenarios. in many situations, messages posted on this platform can provide insights faster and on a more fine-grained level than any other source of information. moreover, it is a gigantic source of opportunistic, and therefore cheap, data. example applications include situational awareness in disaster situations, where twitter users provide updates much faster than official news sources or satellite imagery @xcite ; collection of personal opinions and insights into human behavior, where twitter is faster, more expansive, and cheaper than traditional surveys @xcite ; or mapping of human settlements, where twitter users can provide information that is difficult or impossible to obtain from any other source @xcite . one crucial factor when analyzing twitter posts is the ability to align these messages to places around the world. in disaster situations, knowing what location a certain tweet refers to can be a matter of life or death @xcite . in mapping tasks, information gained from twitter is only valuable when it can be placed on the requested level of detail, e.g. buildings @xcite . on a broader scale, even knowing what city or country a tweet was posted from can provide significant insights into regional differences and developments, such as in the ongoing covid-19 situation @xcite . for these reasons, a lot of social media-focused research relies on geolocations provided within twitter data. up until mid-2019, precise geolocations were reliably available for a sufficient subset of tweets. on june 18th, 2019, however, twitter announced they would remove the option to attach precise geolocations to tweets (geotagging) 1 @xcite . the reasoning given at the time was that not many users were taking advantage of this feature. privacy concerns in connection with precise geolocations have also been voiced in the past @xcite , so there is a possibility that these issues also factored into the policy change. in this paper, we will discuss how the situation has changed and what this means for twitter-based research going forward. besides analyzing the current geolocation situation in twitter data, we aim to shed light on the amount of non-used tweets simply because they do not have point coordinates. although twitter data may not the best choice for fine-grained location based research, as we will see, twitter still represents a treasure trove of geolocated information. whatever we attempt to do with it, we do need to take into account the required and available location granularity. with this paper, we hope to pave the way for more solid and realistic twitter based research. our main contributions are (1) a detailed description of the change and current status, (2) statistics on the availability of different kinds of geolocations, and (3) a detailed reflection on the consequences for various research and how to deal with them. in the next section, we first discuss in detail what exactly twitter's policy change entailed, and present experimental results to determine more closely which geotagging options are currently available and what the resulting data looks like. section 3 provides statistics on the availability of various types of geolocations since 2019. in sections 4 and 5, we analyze what effect the changes have on research, and make suggestions for adapting future research tasks accordingly. section 6 provides a conclusion. ## geolocation availability in the following, we will use the term "geolocated" to mean tweets containing explicit metadata about a geographic location they were posted from or are referring to, and "geotagging" as the user action that causes this metadata to be attached. ## statistics in this section, we perform statistical analyses to gather insights into what geolocation information is currently contained in tweets, and how the policy change impacted the availability of this information. share of geolocated tweets over-all we first calculated the shares out of all tweets that contain any sort of geolocation on a sample collected from the free 1% worldwide twitter stream between august 3 rd and august 9 th , 2021. the results are shown in figure 1 . about .06% of all tweets are geolocated. nearly all of these have a filled place attribute, while only 6% of geolocated tweets provide the coordinates attribute. the last two bars are nearly identical, i.e. if the coordinates attribute is filled, the place attribute is almost certainly also filled. figure 2 : sources of tweets with the coordinates attribute set by month ("total" = all tweets with coordinates collected in the 1% sample of that month). figure 4 : sources of tweets with the place attribute set by month ("total" = all tweets with place collected in the 1% sample of that month). 2020 foot_6 . fortunately, this means that the place attribute is still usable for research. we do see a decrease for instagram crossposts which contain joint coordinates and place, confirming our suspicion in the previous section. secondly, we also considered the place_type field more closely to find out more about the level of detail provided by the place attribute. as shown in figure 5 , the most frequent type is "city" (which is also the level automatically set for instagram crossposts). however, the most fine-grained type "poi" also makes up 1-2% of the tweets, overall resulting in a still relatively large amount of available tweets with a geolocation at this granularity. locations mentioned in text attribute finally, we wanted to obtain a rough estimate of the amount of geolocation information contained in the text attribute of tweets. to this end, we performed named entity recognition on the one-week sample from august 2021 described above. we used a pre-trained huggingface model based on roberta embeddings foot_7 . this model was trained on the conll-2003 @xcite and wikiann data sets with the entity classes taken from wikiann @xcite . it supports 176 languages. figure 6 shows the percentages of tweets that contain any named entity (around 26%) and those with at least one detected loc entity (around 6%). we also show these numbers separately for geolocated tweets: out of tweets with the place attribute, around 32% contained loc entities, while 79% of tweets with the coordinates attribute did. this indicates a stronger semantic focus on the user's location when setting these attributes, but it also means that a large number of tweets mentioning a location has so far not been exploited. while 6% does not sound like a lot, this set of tweets is still around 30 times as large as the number of all geolocated tweets in this sample (compare figure 1 ), and that is only for direct recognition of known geographic entities. we believe this number would be even higher if other clues about location in the text were included. ## effect on research previous research using geolocated tweets has mainly exploited the coordinates attribute under the assumption that it would contain the physi-cal location of the device the tweet was sent from. this approach is easy to motivate -the more finegrained the location, the more information can potentially be gathered from it, even if the task at hand could also be solved with a coarser location. however, even before 2019, there were two disadvantages to this approach. on the one hand, the coordinates attribute is only filled in .01-.05% of the tweets (as opposed to .5-2% for the place attribute). on the other hand, crossposts from other sources filled the coordinates attribute differently. most prominently, the 5-15% of geolocated tweets coming from instagram never contained the gps location of the user. since 2019, the coordinates attribute cannot reliably be used to determine location anymore, as we will detail in the next section. in this section, we will discuss what this means for some common research tasks. as @xcite describe, typical stakeholders of twitter analysis include journalists, civil protection agencies or governing bodies, and businesses. we would add scientists from other domains to this list, leading to the following use cases that have been in the focus of research: pois pois have been in the focus of research for the purpose of recommender systems, detecting novel or unknown pois, analyzing opinions and possible improvements etc. despite the availability of poi-level geolocations in the place attribute, researchers have mostly used the coordinates attribute for this purpose, presumably to gather a wider range of pois that are not dependent on catalogs of geolocation providers @xcite . with the loss of exact gps coordinates, these approaches cannot easily be applied to current data. however, they could easily be adapted to pois provided in the place attribute for many use cases. the exception to this are the discovery of new pois as well as the analysis of user behavior in the vicinity of pois @xcite . mobility another strong focus of social media research is the analysis of human mobility, e.g. travel or commuting patterns. as before, the coordinates attribute has mainly served this purpose to allow for a flexible detection of origins and destinations @xcite . future strategies without this attribute depend on the scale of mobility to be analyzed. when tracking movement between cities or even international travel, the city-level locations in the place attribute should suffice. analysis on the sub-city level is more difficult now. for a general idea, e.g. for transport optimization, poi-level locations could be exploited if a sufficient number of them is available and well-distributed across the area @xcite . in instagram crossposts, a location mapped to the coordinates attribute can also be a street, so this may be a valid source to determine travel in the city (after excluding centroids of other places, e.g. cities). disasters natural and man-made disasters are among the most strongly researched applications of geolocated social media. tasks include the automatic detection of events, detection of tweets related to disasters, classification of such tweets into certain categories, and detection of actionable tweets @xcite . as before, most existing approaches use the coordinates attribute. this is particularly critical for use cases where action is necessary, e.g. calls for help, or where localized developments of a disaster are detected. due to the low share of tweets with exact coordinates even before 2019, efforts have been made to determine such locations from other sources, e.g. @xcite . on the more general level necessary for detecting events and disaster-related tweets in the first place, we believe city or poi locations from the place attribute will often be sufficient. public health similar to the disaster topic, social media has also been suggested to explore public health topics such as the spread of infectious diseases @xcite . for this task, insights are not commonly gained on a sub-city level. previous publications still often exploit the coordinates attribute, but then map it to a city or area. this could easily be substituted with the place attribute. even the user-provided location could be sufficient here as most tasks are not reliant on user location change over short time spans. there are some use cases where this might be necessary, e.g. when attempting to model covid-19 spread on a person-by-person basis, but we would argue that not a high-enough percentage of the population uses geolocated social media to be feasible. marketing marketing tasks on social media include e.g. the analysis of sentiments towards brands or products or the prediction of sales based on user expressions. this appears to be an area where exact geolocations do not serve a purpose and should therefore not see any detriment from the change. in fact, most research so far has focused on analysis without any geo-based statistics or on the country level, e.g. @xcite @xcite . politics and social sciences in politics and social sciences, geolocations are usually not required at a very fine-grained level. the most prominent task in political social media analysis, election prediction, cannot even produce results comparable to the actual election result beyond the city/area level, which is affirmed by the overview provided in @xcite . similarly, empirical analysis of social effects or opinions usually operates on a larger scale, and requires inputs from city areas or larger, e.g. @xcite , this may be an area where the place attribute may be useful at the neighborhood level or lower. a notable exception is @xcite , which focuses on a quantitative analysis of geolocation provision by arabic/muslim users over time influenced by safety concerns. another task where location is crucial is the recognition of suicide risk in users, where recognizing their location could serve to provide help @xcite (also related to the public health topic). ## suggestions for future research as we saw in the previous sections, the availability of geolocations in twitter data has changed quite drastically since 2019. one main takeaway here is that the coordinates attribute, if it is filled, does not signify a user's physical location when they made a post anymore. as of now, this attribute is only available in instagram crossposts, where it is set to the centroid of pre-defined locations coming from instagram. moreover, this was already the case for instagram crossposts before 2019, meaning that for around 10% of tweets, the coordinates attribute never contained the user's gps location in the first place. in the future, researchers should therefore not rely on this attribute as a source of exact geolocation anymore. moving forward, researchers need to carefully consider which level of granularity is necessary for the task at hand. as a general rule, the more finegrained, the less data is available. we suggest the following sources of geolocation depending on the level required: country or city level this is the easiest level to obtain. nearly all geolocated tweets, whether coming from native twitter or from instagram, currently contain location information on the city level or finer in the place attribute. ## conclusion in this paper, we gave a detailed overview over the effects of twitter's geolocation policy change in 2019. we first described the roles of the various tweet attributes provided by twitter's api and how they are filled by twitter itself as well as third-party apps, in particular instagram. we point out a particular issue with the assumption that the coordinates attribute contains the exact location of the user, which has never been the case for instagram crossposts. next, we calculated a range of statistics, including a verification that the amount of tweets with gps locations has starkly decreased since the policy change. we also showed that the place attribute is still usable and more broadly available, albeit less fine-grained, and that the text content of tweets also provides a lot of useful clues to determine geolocation. future research could elucidate the usage of various types of (explicit or implicit) geotagging depending on user demographics. we then discussed the effect on different research tasks and conclude that there are many cases where gps granularity is not necessary, which is also important because of ethical data minimization principles. exceptions include mapping, tasks where users require immediate help in-person, and certain mobility analyses. finally, we suggest what technical steps could be taken moving forward, depending on the required level of geolocation granularity. besides the explicit availability of locations, geocoding approaches based on tweet content are a promising research direction that could unlock a large percentage of the twitter stream for geo-based tasks.