This vision paper summaries the methods of using social media data (SMD) to measure urban perceptions. We highlight two major types of data sources (i.e., texts and imagery) and two corresponding techniques (i.e., natural language processing and computer vision). Recognizing the data quality issues of SMD, we propose three criteria for improving the reliability of SMD-based studies. In addition, integrating multi-source data is a promising approach to mitigating the data quality problems

Liu, Yu

Yuan, Yihong

Zhang, Fan

English

University of Maine

JOURNAL OF SPATIAL INFORMATION SCIENCENumber 20 (2020), pp. 51–55 doi:10.5311/JOSIS.2020.20.665INVITED ARTICLEMining urban perceptions fromsocial media dataYu Liu1, Yihong Yuan2, and Fan Zhang31Institute of Remote Sensing and Geographical Information Systems, School of Earth and SpaceSciences, Peking University, Beijing, China2Department of Geography, Texas State University, San Marcos, TX, USA3Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USAReceived: February 29, 2020; accepted: April 13, 2020Abstract: This vision paper summaries the methods of using social media data (SMD)to measure urban perceptions. We highlight two major types of data sources (i.e., textsand imagery) and two corresponding techniques (i.e., natural language processing andcomputer vision). Recognizing the data quality issues of SMD, we propose three criteriafor improving the reliability of SMD-based studies. In addition, integrating multi-sourcedata is a promising approach to mitigating the data quality problems.Keywords: social media data, urban perceptions, place, data quality, natural language pro-cessing, computer vision1 IntroductionWith the rapid development of the mobile Internet, it is feasible to harvest large volumesof social media data (SMD) with spatio-temporal tags. Such geo-tagged big data provide anew approach to measuring the perceptions of different places at a collective level [12, 14].This new research field is evolving rapidly due to the increasing visibility of social mediaplatforms, such as Facebook, Twitter, Weibo, Foursquare, Flickr, and Yelp. Most geolocatedsocial media posts are from densely-populated urban areas, making it possible to measureurban perceptions and represent human cognition of intra-urban spatial heterogeneity withthe support of SMD [15,18]. These studies provide valuable support in urban planning andpolicy making, and consequently, the implementation of sustainable development goals(SDG) [9].In addition to spatial information (e.g., locations or place names), geo-tagged social me-dia posts usually contain contextual and semantic information, such as text descriptions ofc© by the author(s) Licensed under Creative Commons Attribution 3.0 License CC©52 LIU, YUAN, ZHANGa specific place, activities, or feelings of the users, as well as photos taken at a place [3, 17].Two data mining techniques are widely used to mine SMD. The first is natural languageprocessing (NLP), which extracts activities and emotions of a massive volume of users fromtextual information [1]. Second, thanks to the rapid development of computer vision sup-ported by deep convolutional neural networks (DCNNs), we can conduct content-basedimage analyzes for photos posted on social media.NLP provides an effective measure to not only extract the spatial extent of human ac-tivities, but also to analyze the emotions and experiences associated with a place. This is inline with the definition of a “place” in human geography, where places are defined basedon both the physical and human characteristics of a location [2]. Given that most placeboundaries are vague, previous studies introduced fuzzy sets to represent the spatial ex-tents of places. With the support of SMD, it is feasible to collect posts with particular placenames (e.g., “Harvard Yard”) and compute the kernel density surface of this place name.The resulting density surface can be viewed as a two-dimensional membership functionof a place [6]. In addition, the latent Dirichlet allocation (LDA) model is widely used toanalyze textual information and extract the spatial distribution of topics (e.g., “tourism” or“sport”) from SMD [4]. For example, Ilieva and McPhearson [10] investigated the spatialdistribution of topics on Twitter to represent the geography of urban functional regions inLondon. Areas with a high density of topics like “shopping” or “sales” are likely to bea shopping centre. Researchers also developed methods to extract emotional expressionsand place sentiments from SMD. Some studies used non-negative matrix factorization tomeasure feelings such as depression or happiness. Yang and Mu [19] quantified the degreeof depression using Twitter data collected in New York City and revealed the relationshipbetween depression levels and demographics.Computer vision, especially driven by DCNNs, makes it possible to automatically andefficiently extract and “understand” the content in an image. In addition to photos on socialmedia, street view provides an alternative imagery data source to quantitatively representthe physical and human characteristics of a place, such as demographics, land uses, vi-brancy, and place sentiments. Hence, we can measure certain physical characteristics of en-vironments, such as the greenness and openness of urban spaces, from geo-tagged imagescollected either from social media platforms or street view services [11]. A new researchdirection along this line is to build end-to-end models that can predict particular socio-economic characteristics of interested places. Using street view images, Zhang et al. [21]quantified the perceptions of urban spaces across six dimensions: how safe, lively, beauti-ful, wealthy, depressing, and boring the spaces were. As an example, Figure 1 presents a“safety map” of Houston derived from Google street view images and deep convolutionalnetworks.2 Challenges and future research directionsAlthough SMD provide an unprecedented opportunity to mine urban perceptions, this lineof research encounters several challenges.First, social media data suffer from various data quality issues. In addition to accu-racy and precision problems, sampling biases are inevitable from sociodemographic, spa-tiotemporal, and semantic perspectives [20]. Without a statistically-sound sampling strat-egy, the findings derived from SMD can be questionable. Additionally, social media datawww.josis.orgMINING URBAN PERCEPTIONS FROM SOCIAL MEDIA DATA 53Figure 1: Safety map of Houston. The darker green areas indicate a higher safety scoreinferred by a deep learning model. The model was trained using millions of Google StreetView images rated by individuals worldwide.are considered a type of “thin” data, meaning that although the volume is large, the dataare missing details, such as the background information of social media users [13]. Thisfurther contributes to the difficulty of obtaining a well-balanced sample set from differentpopulation groups, leading to ecological fallacy when aggregating social media posts usingdifferent spatial units. Second, most SMD are used to extract metrics that cannot be directlymeasured or lack ground truths (e.g., the degree of happiness [16]). Hence, it is difficult tovalidate the results obtained from social media analysis. Third, the use of mathematicalmodels introduces uncertainty when dealing with social media data [5]. We often haveto balance the generality and the accuracy of models. If a model is too narrowly defined,the rules for one place may not be generalizable to another place if their socio-economiccharacteristics differ too much.To address these concerns, three criteria, from the least to the most challenging, mayhelp researchers design a sound SMD-based study. First, the data mining methods shouldbe reasonable. For example, LDA is a widely used method to model textual topics, so ap-plying it to analyzing the textual content on Twitter is justifiable [8]. Second, the resultsJOSIS, Number 20 (2020), pp. 51–5554 LIU, YUAN, ZHANGshould be consistent with common sense. Although a certain indicator value (e.g., happi-ness=0.8) cannot be directly validated, one can easily determine whether the results followcommon sense based on the ordinal relations of the metrics between places. If the degreeof happiness of place A is greater than that of B, in general place A should be viewed asmore emotionally positive. Third, a good validation strategy is to use the derived indica-tor to predict another indicator whose actual values can be easily collected or observed.For instance, Gebru et al. [7] extracted car makes and models from street view images andused this information to predict if a voter is more likely to vote for the Democratic or theRepublican Party. The results are verifiable using election data.To mitigate the data quality issues and achieve the above-mentioned criteria, a possiblesolution is to integrate multi-source data. Given that single-source social media data maybe biased, combining various data sources provides complementary information from mul-tiple perspectives and reduces the representativeness biases. Traditional survey-based datacan be very useful in this case because these data are collected from well-designed samplesand contain rich individual-level background information; therefore, they can complementSMD to build more solid models. It is also necessary to develop a statistical framework tointegrate data from different sources and with various quality issues.In the future, the emergence of new communication techniques such as 5G will sig-nificantly increase the network bandwidth and broaden the content that can be shared onsocial media platforms. With the support of high-speed mobile networks, users can easilyshare video clips and three-dimensional point cloud data. These data contain richer infor-mation about the geographical environment compared to texts and images. We need todevelop more powerful tools to extract features from such data and enrich the perceptionsof urban places.References[1] AGGARWAL, C. C., AND WANG, H. Text mining in social networks. In Social networkdata analytics. Springer, 2011, pp. 353–378.[2] AGNEW, J. Space and place. The SAGE handbook of geographical knowledge. SageLondon, 2011.[3] BAO, J., ZHENG, Y., AND MOKBEL, M. F. Location-based and preference-aware rec-ommendation using sparse geo-social networking data. In Proceedings of the 20th inter-national conference on advances in geographic information systems (2012), pp. 199–208.[4] BLEI, D. M., NG, A. Y., AND JORDAN, M. I. Latent Dirichlet allocation. Journal ofmachine Learning research 3, Jan (2003), 993–1022.[5] BOX, G. E., AND DRAPER, N. R. Empirical model-building and response surfaces, vol. 424.Wiley New York, 1987.[6] GAO, S., LI, L., LI, W., JANOWICZ, K., AND ZHANG, Y. Constructing gazetteersfrom volunteered big geo-data based on Hadoop. Computers, Environment and UrbanSystems 61 (2017), 172–186.[7] GEBRU, T., KRAUSE, J., WANG, Y., CHEN, D., DENG, J., AIDEN, E. L., AND FEI-FEI,L. Using deep learning and google street view to estimate the demographic makeup ofwww.josis.orgMINING URBAN PERCEPTIONS FROM SOCIAL MEDIA DATA 55neighborhoods across the United States. Proceedings of the National Academy of Sciences114, 50 (2017), 13108–13113.[8] HONG, L., AND DAVISON, B. D. Empirical study of topic modeling in Twitter. InProceedings of the first workshop on social media analytics (2010), pp. 80–88.[9] ILIEVA, R. T., AND MCPHEARSON, T. Social-media data for urban sustainability. Na-ture Sustainability 1, 10 (2018), 553–565.[10] LANSLEY, G., AND LONGLEY, P. A. The geography of Twitter topics in London. Com-puters, Environment and Urban Systems 58 (2016), 85–96.[11] LI, X., ZHANG, C., LI, W., RICARD, R., MENG, Q., AND ZHANG, W. Assessingstreet-level urban greenery using Google Street View and a modified green view in-dex. Urban Forestry & Urban Greening 14, 3 (2015), 675–685.[12] LIU, Y., LIU, X., GAO, S., GONG, L., KANG, C., ZHI, Y., CHI, G., AND SHI, L. Socialsensing: A new approach to understanding our socioeconomic environments. Annalsof the Association of American Geographers 105, 3 (2015), 512–530.[13] LONGLEY, P. A., AND ADNAN, M. Geo-temporal Twitter demographics. InternationalJournal of Geographical Information Science 30, 2 (2016), 369–389.[14] MACEACHREN, A. M. Leveraging big (geo) data with (geo) visual analytics: Place asthe next frontier. In Spatial data handling in big data era. Springer, 2017, pp. 139–155.[15] MARTÍ, P., SERRANO-ESTRADA, L., AND NOLASCO-CIRUGEDA, A. Using locativesocial media and urban cartographies to identify and locate successful urban plazas.Cities 64 (2017), 66–78.[16] MITCHELL, L., FRANK, M. R., HARRIS, K. D., DODDS, P. S., AND DANFORTH, C. M.The geography of happiness: Connecting Twitter sentiment and expression, demo-graphics, and objective characteristics of place. PloS one 8, 5 (2013).[17] ROICK, O., AND HEUSER, S. Location based social networks—definition, current stateof the art and research agenda. Transactions in GIS 17, 5 (2013), 763–784.[18] SHELTON, T., POORTHUIS, A., AND ZOOK, M. Social media and the city: Rethinkingurban socio-spatial inequality using user-generated geographic information. Land-scape and urban planning 142 (2015), 198–211.[19] YANG, W., AND MU, L. GIS analysis of depression among Twitter users. AppliedGeography 60 (2015), 217–223.[20] YUAN, Y., LU, Y., CHOW, T. E., YE, C., ALYAQOUT, A., AND LIU, Y. The missingparts from social media-enabled smart cities: Who, where, when, and what? Annals ofthe American Association of Geographers 110, 2 (2020), 462–475.[21] ZHANG, F., ZHOU, B., LIU, L., LIU, Y., FUNG, H. H., LIN, H., AND RATTI, C. Mea-suring human perceptions of a large-scale urban region using machine learning. Land-scape and Urban Planning 180 (2018), 148–160.JOSIS, Number 20 (2020), pp. 51–55

Mining urban perceptions from social media data

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Mining urban perceptions from social media data

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