From Where Do Tweets Originate? - A GIS Approach for User Location Inference Qunying Huang

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1 From Where Do Tweets Originate? - A GIS Approach for User Location Inference Qunying Huang Department of Geography University of Wisconsin-Madison Madison, Wisconsin, qhuang46@wisc.edu Guofeng Cao Department of Geosciences Texas Tech University Lubbock, TX guofeng.cao@ttu.edu Caixia Wang Department of Geomatics University of Alaska Anchorage Anchorage, AK cwang12@uaa.alaska.edu ABSTRACT A number of natural language processing and text-mining algorithms have been developed to extract the geospatial cues (e.g., place names) to infer locations of content creators from publicly available information, such as text content, online social profiles, and the behaviors or interactions of users from social networks. These studies, however, can only successfully infer user locations at city levels with relatively decent accuracy, while much higher resolution is required for meaningful spatiotemporal analysis in geospatial fields. Additionally, geographical cues exploited by current text-based approaches are hidden in the unreliable, unstructured, informal, ungrammatical, and multilingual data, and therefore are hard to extract and make meaningful correctly. Instead of using such hidden geographic cues, this paper develops a GIS approach that can infer the true origin of tweets down to the zip code level by using and mining spatial (geo-tags) and temporal (timestamps when a message was posted) information recorded on user digital footprints. Further, individual major daily activity zones and mobility can be successfully inferred and predicted. By integrating GIS data and spatiotemporal clustering methods, this proposed approach can infer individual daily physical activity zones with spatial resolution as high as 20 m by 20 m or even higher depending on the number of digit footprints collected for social media users. The research results with detailed spatial resolution are necessary and useful for various applications such as human mobility pattern analysis, business site selection, disease control, or transportation systems improvement. Categories and Subject Descriptors J.4 [Computer Applications]: Social and Behavioral Sciences General Terms Human Factors, Experimentation Keywords Human Mobility, Spatial clustering, Spatiotemporal Clustering, Big Data, Geography Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ACM SIGSPATIAL LBSN 14, November 4, 2014, Dallas, TX, USA Copyright (c) 2014 ACM ISBN $ INTRODUCTION Social media, such as Twitter, Google+, Foursquare, LinkedIn, Facebook, and Flickr, is now widely used to support different applications with significant societal impacts. Examples include disease outbreak detection [1], emergency management and relief [2-3], analysis and mapping of people s opinions or sentiments towards to political and social events [4], and human mobility patterns [5-6]. This is because social media data are rich in content, capturing many aspects of individual lives, experiences, behaviors, and reactions to a specific topic or event. In addition, social media data are widely available on a timely basis. For example, for Twitter alone, the number of tweets has reached 400 million per day tweets were posted every second in March, 2013 and that number is escalating rapidly [7]. User locations, however, are vital to many of the applications stated earlier. For example, disease control managers need to know the source of flu virus in order to develop effective control strategies. Many researchers have therefore been devoted to the task of estimating the geographic true origin of a message. Scholars [5, 8], for instance, estimated city-level user location purely on the content of the tweets without the use of any external information, such as a gazetteer, IP information, a geo-tag, etc. Those approaches are therefore known as text based approach and additionally do not consider the underlying social interaction, i.e. the structure of interactions between the users. For example, Cheng et al. [5] proposed and evaluated a probabilistic framework for estimating a Twitter user's city-level location based only on the content of the user's tweets. Chandra et al. [8] calculated a baseline probability estimate of the distribution of words used by a user. This distribution is formed by using the fact that terms used in the tweets of a certain discussion may be related to the location information of the user initiating the discussion. The authors [8] then estimated the top K probable cities for a given user and measured the accuracy. In addition to text content, many studies for user location estimation gradually integrated online social profile, behaviors and interactions of users for geospatial cues, such as address information presented in the user profile or by the user s friends [9], to better predict user location with higher accuracy and more details [10, 11]. These studies demonstrated a user's country and state can be easily determined with decent accuracy, as well as a user s city inferred with an accuracy around 70% to 90%, depending on different algorithms [10]. Mahmud et al. [11], for instance, used an ensemble of statistical and heuristic classifiers to predict home locations of Twitter users at different granularities, including city, state, and time zone, based on the content of their tweets and their tweeting behavior. In that paper, nouns, hashtags and place names are extracted as features. Experimental evidence

2 suggests that this algorithm works well in practice and outperforms previous algorithms [5] for predicting the location of Twitter users. Li et al. [9] introduced a probabilistic model integrating geospatial cues observed from both social network (friends) and user-centric data (tweets). Pontes et al. [12] applied a finer-grained inference that reveals the geographic coordinates of the residence of a selected user group, achieving up to approximately 60% accuracy within a radius of six kilometers. Integrating more attributes that could be associated with potential geographical cues into a location inference model could possibly achieve higher accuracy but not higher granularity (i.e., not greater detail). Additionally, such models have been constrained in a number of important ways [13]. One particular issue posed by these text based approaches is data quality. These studies assumed that geospatial cues in a text are indicative and reliable in terms of its author s location while most analyzed attributes (e.g., place names hidden in the content or user profile) include errors and are biased in nature. These text-based attribute values are filled by users in an open text field without any automatic verification. Thus, noises due to invalid locations, misspelling or even nonsense words may appear. As social media applications are widely deployed in the mobile platforms with built-in GPS tracking devices, more and more social media datasets consist of geo-tags (geographic coordinates). These geo-tags, along with place names, offer a new opportunity to infer individual trajectories and locations to a much more accurate level. Instead of using the hidden geographic cues widely used in current text-based models, this paper develops a GIS approach that can infer the true origin of tweets down to the zip code level by using and mining spatial (geo-tags) and temporal (time when the content was posted) information recorded in user digital footprints. Additionally, individual major daily activity zones, which are grouped in this paper into eight categories, such as office, entertainment, residential, etc., and movement trajectories can also be inferred and predicted. Within this approach, spatial clustering analysis is first applied on the geotags of tweets to find out several representative activity zones that the user frequently visited. Temporal analysis is then used to examine activity temporal patterns of these representative zones. Finally, the specific type for each representative physical activity zone can be inferred through integrating GIS land use data and surrounding environmental information of the representative zones returned from the Google Places service [13]. Initial studies and experiments over selected users demonstrate that the proposed method can successfully infer individual daily physical activity zones with spatial resolution as high as 20 m by 20 m or even higher depending on the number of digit footprints collected for social media users. The rest of the paper begins with a description of the methodology for spatiotemporal mining digital footprints of social media users. Section three presents the user case studies. The paper is concluded with discussions about issues, challenges and future research directions of using social media data for human mobility analysis and study. 2. METHODOLOGY This section first introduces methods of user location inference based on geo-tags and temporal information, starting from social media data collection and organization in spatial databases. The problems in examining citizens mobility based on their online digital footprints are then discussed. Finally, we present a model to mine spatiotemporal patterns during a certain period of social content providers and trace their periodic trajectory at different resolution levels. 2.1 Data Collection and Integration This research adopts tweets as the primary original data source for discovering potential spatiotemporal patterns of human activities because Twitter is so far one of the most popular social media networks with massive numbers of international users. In this work, the social media datasets with spatial and temporal information come from several representative, frequently-posting Twitter users. These datasets are harvested from Twitter public stream using representational State Transfer (REST) application programming interfaces (APIs) over a long period, with no less 50 geo-tagged tweets per user to ensure the data quality. 2.2 Problem Statement In our model, the online social media activity record for a user is defined as a sequences S in form of {(L 0, T 0, C 0 ), (L 1, T 1, C 1 ), (L 2, T 2, C 2 ),, (L n, T n, C n )}, where L i represents the geo-location [latitude, longitude] of the user who posted a message with specific contents C i at time T i [second/minute/hour/ day/month/year]. Multiple records per user may share the same location because the user didn t move. However, the content created time (T i ) is the unique attribute to distinguish each activity for a particular user. Given such a sequence, our goal is to build a model for discovering citizens daily mobility including individual primary activity zones (PAZ) and activity zone types (AZT) in space, and users activity patterns in the time dimension. In our study, the daily mobility of citizens can be defined as: {(LC 1, ZT 1, TS 1, R 1 ), (LC 2, ZT 2, TS 2, R 2 ), (LC 3, ZT 3, TS 3, R 3 ), (LC m, ZT m, TS m, R m )} where LC i represents the center point of activity zone, ZT i is the type of the zone, TS i represents the time periods, and R i is the radius of zone circle measuring user s tweet numbers within i zone. 2.3 Spatiotemporal analysis model for user location inference In this study, a GIS based spatiotemporal analysis approach is proposed to mine citizens daily activity zones based on digital footprints recorded by online social activities. The approach is comprised by three primary components: spatial clustering, temporal analysis, and user activity zone inference. The workflow of integrating different components for user location inference is illustrated as follows (Figure 1): Geo-tagged tweets Detect activity zone types Spatial clustering Place Names Place Types Temporal analysis Google places service Representative points Spatial Join Points with land use type attribute Figure 1. Workflow of user location inference Land use GIS data

3 Spatial clustering: While exploring a user s activity spatial patterns, spatial clustering analysis is performed on geo-tags of tweets to find several representative activity zones where the user frequently posted content. Given a user s digital footprints, a series of clusters as representative activity zones can be produced. A center point LC i with a pair of latitude, longitude for each representative activity cluster can be calculated as the representative point based on the points within that cluster. Temporal analysis: In the time analysis component, statistical methods are used to examine activity temporal patterns of each representative activity zone. After this step, each representative point will be attributed temporal information TS i. Activity zone type inference: Finally, the specific type for each representative activity zone can be inferred through integrating GIS land use data, the spatiotemporal information of each representative center point, and geoplaces of the center point returned by the Google Places service API, which provides land use types of nearby places within a predefined center and radius[13] Spatial Clustering Individuals typically have a regular movement trajectory in space during daily life. Individuals typically have a regular movement trajectory in space during daily life (an example is modeled in Figure 2). If a location service is enabled on the mobile device while posting the online message, a series of digital footprints would be recorded at different places, such as office, home, restaurants, parks, etc., where an individual appears frequently. Figure 2. Individual daily movement flow In this study, to detect representative activity zones from digital footprints, density based spatial clustering of applications with noise algorithm (DBSCAN), a well-proved classic clustering algorithm, is applied. DBSCAN can discover potential arbitrary shape areas grouped with high-density points with similar characteristics [15]. Since people usually visits at some places regularly in daily life, the tweet records they publish should be aggregated at these places. In other words, DBSCAN algorithm is more appropriate than other clustering algorithms to perform clustering on these high-density spatiotemporal points over space and time. Additionally, as compared with other commonly used methods, such as K-means, DBSCAN does not require a prespecified K value (the number of clusters), which is usually arbitrarily assigned through an interactive process. After performing spatial clustering analysis, a set of clusters can be produced to indicate the representative activity zones that an individual would typically visit Temporal Analysis In addition to regular patterns in space, an individual s activities could also show patterns in time as human typically follows a relatively restricted schedule, performing different activities in various locations at different times. In this work, temporal analysis is designed to perform detailed statistics on the individual representative activity zones detected in the spatial clustering component over different time periods. It is also designed to analyze the inherent movement trajectories of Twitter users based on the temporal information in the time of tweeting. Specifically, the temporal analysis aims to find the time period (TS i ) corresponding to each representative location to examine the temporal variability of user s behavior on a particular location. Figure 3. Temporal clusters in 24 hours The temporal analysis is implemented based on DBSCAN algorithm, but uses temporal resolution as the measurement distance to find neighbors within a group rather than the Euclidean distance between two points. Temporal resolution is defined as the temporal distance, in which all tweets published within a predefined continuous time segment, e.g. 6 minutes (0.1 hour), are grouped as a cluster. This approach can effectively detect several time segments within one day period during which intensive online activities occur in daily life. Figure 3 shows an example of a user with 1195 geo-tagged tweets collected within a half year, 257 between 10:40AM 14:45PM, 427 between 15:00PM 19:58PM, 63 between 20:00PM 21:30PM, 94 between 21:30PM 23:12PM, and 23 of them between 0:15AM 0:55AM. The temporal distribution of social media datasets accurately reflects individual behavioral habits in daily life and helps explore citizens mobility over time User activity zone types In section 2.3.1, a variety of spatial clusters representing individual primary activity zones can be detected through spatial clustering on all spatiotemporal points during a period of time, e.g., one year or more. Meanwhile, as discussed in section 2.3.2, analyzing the temporal information of these clusters makes it easy to determine the time range in which individual activities mostly occur. To explore the accurate citizen trajectories, it is necessary to identify the types for each activity zone, which can help us further understand individual spatiotemporal movement patterns. Considering common daily activities and referencing the GIS land types [17], Google place types [16] and related studies [18], eight activity zone types are defined in this work to explore user trajectories and activities (Table 1). The categories, shown in the left column of Table 1, include office, transportation, education, eating, health, shopping, entertainment, service, and residential. The eight zone types include most of the daily activity regions that an individual would visit in an urban area at the level of zip code. The movement

4 trajectory connecting these activity zones at specific times reveals an individual s mobility over space and time. Table 1. Mapping between google place types and human activity zones Activity zone types Office Transportation Education Eating Health Shopping Entertainment Service Supported google place types accounting, establishment, finance, fire station, bank, general contractor, police, post office, city hall, courthouse, local government office, lawyer, embassy Airport, parking, taxi station, train station, travel agency, subway station, rv (recreational vehicle) park, bus station Campground, school library, university, college Bakery, food, restaurant, meal delivery, meal takeaway, café Pharmacy, physiotherapist, dentist, doctor, hospital, health Bicycle store, book store, clothing store, convenience store, florist, pet store, furniture store, grocery, supermarket, hardware store, department store, store, electronics store, liquor store, shoe store, shopping mall, home goods store, jewelry store, Amusement park, aquarium, art gallery, night club, painter, park, gym, hair care, casino, museum, movie theater, spa, stadium, zoo, bar, beauty salon, bowling alley Car dealer, car rental, car repair, car wash, ATM, funeral home, gas station, place of worship, plumber, cemetery, church, real estate agency, Hindu temple, roofing contractor, insurance agency, moving company, electrician, laundry, storage, synagogue, veterinary care, lock smith, lodging, mosque, movie rental Inferring activity zone s type Land use data is very useful GIS data that indicates the classification of various types of public or private land usage. It is worth noting that urban land use data from different cities might have different categories. Figure 4 shows an example of land use data for exploring users located in St. Louis, Missouri [19]. It is comprised of a set of polygons representing land parcels, which include six land use categories, such as residential, and business and industry. However, urban land use or place types are typically divided into seven categories, including residential, commercial and services, industrial, transportation and communication and utilities, industrial and commercial complexes, mixed urban or built-up land, and other urban or built-up land [17]. The land use types can be effectively used to detect the types of regular activity zones where an individual may present in daily life and are indicated in the digital footprints. By analyzing the land use types of where the digital footprints are generated, the zone types of individual activities can be inferred. Figure 4. Urban land use map of St. Louis, Missouri It should be pointed out that common GIS land use types typically aggregate places used for different activities (e.g., eating and entertainment) into the same category (commercial) (Figure 4). Therefore, the land use data alone is insufficient to explore common human activity zones. In addition, the mixed urban in land use category, is ambiguous to use for investigating human activity. Fortunately, commercial location service providers, such as Google, Microsoft, and Yelp, separate places (locations) into more detailed categories of point of interest (POI). For example, Google categorize places into 96 types (Table 1), and its Google Places service will return one of those types given a location service query. However, Google places do not support residential category, which is one of the most important activity zones of a Twitter user. Table 2. Mapping between urban land use types and human activity zones Urban land use type Residential Commercial and Service Industrial Transportation, communications and utilities Industrial and commercial complexes Mixed urban or build up land Other urban or built-up land Human activity zone type Residential Education, Health, Shopping, Eating, Entertainment, Service Office Transportation, Service Office, Entertainment, Shopping, Eating, Service Home, Service, Education, Shopping, Eating, Entertainment, Health, Office Unknown

5 To overcome the issues posed by both GIS land use data and commercial location services for user location inference, this work integrates both data sources. Based on the classification of urban land use types and human activity zone types, we detect the activity zone types as follows. The first step is to find out the usage type of the urban land where the selected activity point is located in order to infer the possible activity zones in which it occurs according to Table 1. We perform the spatial join operation, a classic GIS function, between the Twitter representative center points produced in section and the land use polygon layer. The land use type for each point is detected and then, mapped to possible general aggregated activity zone types through the mapping rules defined in Table 2. For example, if a point is detected within a polygon representing commercial land, it suggests that the user who published the tweet may be eating at a restaurant, shopping at a mall, having a cup of coffee in a cafe, or having fun at a nightclub. However, the question what exactly the user was doing? is still not answered. Revealing more detailed information about user s activity is implemented in the second step, geo-location mining based on Google Places API which is discussed in the next section Detailed Geo-location Mining based on Google Places API Given a geographic referenced point, no matter in the format of address or coordinates (latitude and longitude), it can be easily georeferenced on a map, such as Google Maps or Bing Maps. By manually interpreting the map through common clues of the objects in the community, e.g., size, shape, pattern or texture, we can establish a general understanding about its region or community types (or zone types in other words), such as residential homes, business community, entertainment places, school zones, and hospital sites. However, such manual interpretation is time-consuming, ineffective and inefficient. To determine the specific location type of activity zones, a more automated and efficient approach has been developed based on the Google Places API. Given the coordinates of a representative point for each activity zone and GIS land use data calculated in the spatial clustering step, the specific land use type of the point can be detected based on the spatial union operation. A HTTP request is then sent to the Google Places service [14] for querying nearby places. When constructing the Google places service query, two required parameters, a geo-location or address and a radius of searching circle (in meters) within which place results are returned, need to be configured. In this work, since we are interested in the surrounding environment of the representative point, the center of query is configured as the location of our representative point. The radius size is arbitrary with a trade-off. In general, the larger the radius is, the more points of interest around the location can be returned for analysis. On the other hand, if the size of radius is too large, the responses may include many unrelated places. Thus, choosing the radius size must be considered carefully, and trialand-error may be needed. Our trial-and error experimental studies suggested that a radius of 20 meters is a generally appropriate radius size. A response result from the Google Places service includes a set of places defined by a variety of parameters. Usually, the result contains information about an array of place objects. The Places API returns up to 20 establishment results per query. A sample place in the format of JSON (JavaScript Object Notation), which is a lightweight data-interchange format over the internet, in the response is described as below. {"formatted_address": "529 Kent Street, Sydney NSW, Australia", "geometry" : { "location" : { "lat" : , "lng" : } }, "icon" : " urant-71.png", "id" : "827f1ac561d72ec25897df f7cbbc8ed", "name" : "Tetsuya's", "rating" : 4.30, "reference" : "CnRmAAAAmmm3dlSVT3E7rIvwQ0lHBA4sayvxWEc4 nz", "types" : [ "restaurant", "food", "establishment" ] } A typical place name may include many fields such as formatted address, geometry, icon, id, types, etc. The field types is important since it provides indicative information about the activity zone type of the representative point. For each place, the field types is therefore extracted to detect activity zone types for the representative cluster. As introduced in the section 2.3.3, a mapping rule (Table 1) is developed to project the 96 Google place types into related activity zone types, such as office, shopping, etc. However, Google place types do not directly provide residential as one of its field type values. We discovered another field name may offer indicative information to infer a representative zone as residential type. In this work, if the value from the name field contains a set of key words, such as apartment, condo and house, which indicates a dwelling area, the place is categorized as residential. Among all residential zones detected, one residential zone could be the user s home region while others could be the homes of friends or relatives. In our paper, the place with the highest density of tweets and longest time span is selected as home from several residential clusters. We argue that this is reasonable as individuals will conduct more online social activities at home than other places. 3. EXPERIMENTAL RESULT AND ANALYSIS In this study, 1259 geo-tagged records of a Twitter user living in St. Louis City, Missouri, were collected over the span of one year, and land use GIS data for St. Louis were downloaded as a shapefile from the St. Louis City Strategic Land Use website [19]. Both social media datasets and land use data are organized and managed in a PostgreSQL spatial database. Three experiments are designed to test the feasibility of the proposed approach in exploring user activity locations with recorded digital footprints: spatial clustering, temporal analysis, and detecting user activity zone types.

6 3.1 Spatial clustering Spatial clustering over digital footprints enables us to detect spatial-aggregated sites where users regularly appear in daily life. The spatial distribution of these sites significantly indicates the spatial pattern of the user s trajectories. Additionally, the number of footprints at a particular site suggests how often the user visits each location. The research performs the spatial clustering algorithm, DBSCAN, on all geo-tagged tweets collected from the selected Twitter user and detects 11 representative activity zones (clusters), where more than 6 tweets are published. Figure 5 shows spatial distribution and the number of tweets for all zones (sites) in a 3D geo-visualization map. It can be observed that there are three most active places where the user frequently appears that contain most of the digital footprints. Figure 6. Temporal clustering using DBSCAN 3.3 Inferring activity zone s type Figure 5. Geo-tagged footprint distribution of a Twitter user in St. Louis, Missouri (The length of bars is proportional to the tweet number at each representative location) 3.2 Temporal analysis Temporal analysis explores the temporal pattern of citizens digital footprints at clustered locations from social media datasets featured with temporal information. In this study, experiments on DBSCAN time analysis are conducted to explore the temporal pattern of the selected user s digital footprints when these footprints occurred in the same activity zone. The concept of time distance is deployed to extract the continuous temporal segments (clusters) for representative spatial clusters. Therefore, the data for performing each temporal clustering is restricted to one representative zone. In this study, only dominant sites (S1, S2, S3, and S4) including most of points (more than 50 tweets per site) were selected for temporal clustering. Figure 6 shows the start and end time of the time span that the individual may visit, and the beginning and finish time of the temporal segments (clusters) derived for each dominant site (s1 to s4) to represent the time ranges or spans that the individual would mostly visit at this particular site during a day. It can be observed that only one the temporal segment from 1:50AM to 04:10AM is derived for site S4. In fact, a large portion of points (96 out of 205) at S4 site fall into the time window. However, three time segments are detected based on spatiotemporal points at S2, including 0:10AM - 3:45AM, 12:50PM - 13:15PM, and 14:00PM - 14:40PM with each time segment consisting of 177, 20, and 40 points respectively. Figure 7. User tweet spatiotemporal distribution and user activity zone classification (The width of the bar is proportional to the tweet number at each representative site; sections in bars depicted in purple color indicate temporal segments derived for each site) Depending on GIS land use data and Google place data, the type of user activity zones can be effectively detected and recognized through analyzing the surrounding place types of each representative zone (site) from Google places services. In this experiment, the zone types of the four primary activity zones derived for the selected user include one residential type (home), one office type, one entertainment type, and one eating type. Figure 7 shows the spatiotemporal distributions of those primary activity zones in a 3D map over space and time. In the map, different colors are used represent different pre-defined types of zones derived from spatial clustering. Each zone is depicted as a bar with different colors and is placed between the start and end time boundary detected. The width of the bar is proportional to the tweet number at each representative site. It is apparent that the user posts more messages at home demonstrated by bar with

7 larger width. Additionally, temporal segments (clusters) are depicted in purple colors for each zone. According to the yellow bar in the 3D map, it clearly shows the location of user s primary eating site, and the time segment (s) that the selected user frequently visits the site. It can also be observed that the office (blue color) and home (red color) for the selected use are located at the west and east of the town respectively, while the primary eating and entertainment sites are sitting between them. It makes sense, since people would visit restaurants or bars for meals or entertainment between the working and home locations. 4. CONCLUSION AND FUTURE WORK In this study, a GIS approach is proposed to infer the types of social activity zones of social media users. In addition to social media data, this approach integrates multiple source datasets, including land use data, and internet-based geo-objects from Google Place services, to detect the user activity zone types through spatial and temporal analysis. Two steps are developed over these datasets to infer zone types: determining the type of locations where online social activities occur using urban land use data and mining geo-location based on the Google Places API. These research results can be applied to many areas. Examples could be business site selection, advertisement for targeting customers, disease control, urban planning or transportation systems improvement. For example, one of the online social activities patterns discovered during the study showed that users likely send messages at several specific spots with heavy traffic jams. Therefore, traffic jam hotspots could be determined for transportation departments by studying massive online user mobility and behavior patterns within a city. To infer a user s location, this study requires a certain number of geo-tag enabled tweets, while the number depends on the spatiotemporal behaviors of the tweet users. If a user tends to tweet from the home frequently, the proposed model can infer the home location of the user. If a user has a sparse digital footprints in both space and time dimension, it is unlikely that the model can predict accurate results. During our study, we find that 50 geotagged tweets on average are required. However, many users are unwilling to insert geo-tags into the message in the time of tweets due to the privacy issues. To produce more promising results, more effort should be devoted to integrating geo-tags and other traditional geospatial cues that are extracted from content text, user profiles, and behaviors and interactions with friends in the inference model. It is also worth noting that spatiotemporal mining of massive datasets are time-consuming. For example, the computing cost of the DBSCAN algorithm used for spatial cluster detection is O (n 4 ) where n represents the number of clustered points; it is computing intensive, especially when many users locations must be inferred. Therefore, parallel computing [20] and the latest computing models such as cloud computing [21] should be applied into the proposed model. 5. ACKNOWLEDGMENTS We would like to thank Caitlin McKown and David W.S. Wong for the help and support during this research. 6. REFERENCES [1] Sakaki, T., Okazaki, M., and Matsuo, Y Earthquake shakes Twitter users: real-time event detection by social sensors. 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