Capturing citizen voice online: Enabling smart participatory local government

Cities 95 (2019) 102400

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Capturing citizen voice online: Enabling smart participatory local government

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Tooran Alizadeh , Somwrita Sarkar, Sandy Burgoyne Sydney School of Architecture, Design and Planning, The University of Sydney, Australia

A R T I C LE I N FO

A B S T R A C T

Keywords: Citizen engagement Social media Twitter Sentiment analysis Topic/cluster analysis Crowdsourcing

Social media and online communication have changed the way citizens engage in all aspects of lives from shopping and education, to how communities are planned and developed. It is no longer one-way or two- way communication. Instead, via networked all-to-all communication channels, our citizens engage on urban issues in a complex and more connected way than ever before. So government needs new ways to listen to its citizens. The paper comprises three components. Firstly, we build on the growing discussions in the literature focused on smart cities, on one hand, and social media research, on the other, to capture the diversity of citizen voices and better inform decision-making. Secondly, with the support of the Australian Federal Government and in collaboration with the local government partners, we collect citizen voices from Twitter on selected urban projects. Thirdly, we present preliminary findings in terms of quantity and quality of publicly available online data representing citizen concerns on the urban matters. By analyzing the sentiments of the citizen voices captured online, clustering them into topic areas, and then reevaluating citizen's sentiments within each cluster, we elaborate the scope and value of technologically-enabled opportunities in terms of enabling participatory local government decision making processes.

1. Introduction Amidst the speedy growth of smart city promises and practices, there is an urgent need to take a critical approach and offer an integrated vision for an otherwise fragmented and sectoral concept of ‘smart’ (Borsekova & Nijkamp, 2018). In particular, the literature warns about the lack of citizen voices in smart city decision making processes, and projects (Alizadeh, 2018; Lara, Costa, Furlani, & Yigitcanlar, 2016; Niaros, 2016). Indeed, the current debates around smart cities are full of contradictions. On one hand, there are near-utopian notions around the concept of ‘smart’, with the big tech-companies projecting visions of perfectly optimized and smooth running lives in perfectly organized cities, a sort of a return to the concepts of the ‘city as a machine’. On the other hand, the same utopia-based vision turns dystopic, when it is pitched against the worst that it could become, the city turning into an omnipresent, omniscient Truman show (Weir, 1998) or an Orwellian 1984 (Orwell, 1949), with ironically the big-smart-tech companies running the show (Valverde & Flnn, 2018). In the middle of both extremes is a worrying lack: there is no space in which the everyday messiness, and the infinite capacity that a ‘good’, ‘fair’, ‘just’ or ‘sustainable’ city must have for adaptability to collective actions, choices and lives of the citizens, is discussed, conceptualized, acknowledged, or ⁎

captured. In other words, a framework for citizen engagement in the design and planning of a city is an essential need in a fully functioning healthy democracy (Dong, Sarkar, Nichols, & Kvan, 2013). In reality, such a city would likely be a middle ground between being completely top-down, system-driven versus completely bottom-up, citizen-driven. This paper makes a beginning by acknowledging this middle space between the two extremes, a space where citizen voices may provide a bridge between the top-down, system-driven city and the bottom-up, people-driven city. Further, there are serious questions about how collective citizen voices can be accounted for in the urban development processes already in place, using smart technological advances already at hand (Fernandez-Anez, Fernández-Güell, & Giffinger, 2018). In developing a response to such questions, the paper firstly acknowledges the limitations (including but not limited to the demographic bias) involved with collecting citizen voices from one or limited number of channels (more on this follows in the paper); and secondly proposes an additional channel to listen to the citizens in the age of telecommunication and social media. The paper starts with a portrayal of the bifurcated smart city landscape, presents the ways in which citizen voices can be captured via social media using crowdsourcing techniques, and identifies the

Corresponding author. E-mail address: [email protected] (T. Alizadeh).

https://doi.org/10.1016/j.cities.2019.102400 Received 6 November 2018; Received in revised form 26 June 2019; Accepted 7 July 2019 0264-2751/ © 2019 Elsevier Ltd. All rights reserved.

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On a positive note, however, the absence of real citizen involvement and participation has encouraged a push for an alternative version of smart cities to provide a counter-point to the corporate vision (Hollands, 2015b; Kostakis, Bauwens, & Niaros, 2015). This alternative vision has emanated from small-scale and fledgling examples of participatory community-based type smart initiatives (Chatterton, 2013; Niaros, 2016; Radywyla & Biggs, 2013). Previous studies (Alizadeh, 2018), in search of common ground for the growing number of alternative smart initiatives, have put the below list as the core elements that tie together the alternative smart city vision:

shortcomings that motivated our study. This leads to the second part of the paper, in which the methodological details of our study – collecting citizen voices in collaboration with local government partners – are included. In the third part of the paper, we present some of the tentative findings in terms of quantity and quality of publicly available online data representing citizens' voices and their concerns on local urban matters. The paper concludes by elaborating the scope and value of technologically-enabled opportunities in terms of enabling participatory local government decision making processes. The paper's novelty and its contributions the existing literature are three fold. In particular, the conceptual, methodological, and empirical advances proposed in the paper relate to exploring the use of social media as a data source for informing urban planning. While in several other fields, social media data is regularly used for scientific and social research, to understand social, economic, and historical processes, there is as far little use of it evident in the fields of urban design and planning. First, as a methodological and empirical advance, we introduce a new application for collecting and analyzing social media data by employing existing peer reviewed data mining and machine learning algorithms, with a specific focus on developing understanding citizen sentiments on common local planning issues, via publicly available social media platforms. In a time that most of customer or citizen opinion tools and platforms solely focus on sentiment analysis (e.g. asking respondents how happy or unhappy they are with a service in a commercial domain like rating of movies or books); we have taken the steps to go beyond while applying this idea to the problem at hand. By combining cluster/ topic analysis with sentiment analysis in our analytical tools, we give local governments a deeper understanding of the main issues/topics discussed by citizens in the online domain (more on this follows in the paper). Second, as a conceptual advance, we do so in collaboration with partner local governments on the ground interested in finding a way forward, to enable public engagement in smart city debates and decision making. This combination enables academic research to have a real on-ground impact in changing planning processes, or at least explore the beginning of the process. Partner local governments' involvement with the study is a testimonial to the necessity and perceived need of such tools at local planning level when resources to build meaningful citizen engagement are scarce. In a sum up, lessons learned contribute to the fast-growing and yet understudied fields of empirical smart city studies; and also applied social media research. Moreover, findings inform governments of all levels of the opportunities and challenges involved in capturing meaningful public insights online.

– An emphasis on citizen engagement beyond the simple delivery of services – A democratic bottom up approach: to promote participatory urban technologies, greater social inclusion, and a substantial shift in power from corporations to ordinary people and their communities – Reliance on dynamic public-private partnership: with an emphasis on participatory governance rather than an entrepreneurial one – A tendency to identifying the urban problem first, and only then reaching out for the relevant technological solution: with emphasis on the capacities of each city, and its distinct cultures, histories, and political economies – Is associated with the free software and open access movement and – Is in the preliminary phase: far from being mature; and mainly exist in seed form. We will revisit this list, later in the paper (in the Section 3.4), to show how our study aligns with the alternative smart city vision. 2.2. Power of crowd via social media An essential element of the alternative smart city vision is an emphasis on citizen engagement by empowering their voices (Lee, Hancock, & Hu, 2014; Papa, Gargiulo, & Galderisi, 2013). However, participatory planning literature acknowledges the difficulties involved in gathering people's voices in urban decision making processes (Davies, Selin, Gano, & Guimarães Pereira, 2012; Umemoto, 2001). There are, however, two relatively new phenomena – social media and crowdsourcing – which provide opportunities for smart technologies and techniques to capture citizen voices via alternative channels (Kleinhans, Ham, & Evans-Cowley, 2015). Below we briefly discuss these two phenomena. It should be highlighted that we acknowledge the demographic bias of social media users - especially in terms of age (Sloan, Morgan, Burnap, & Williams, 2015); and the aim is to introduce a full representation of society. Instead, social media is considered as a new channel to engage with citizens, to complement the already existing public participation mechanisms (Willems & Alizadeh, 2015). The brief discussions below do not aim to offer an all-inclusive account around the complexity of social media and crowdsourcing but rather to provide the foundation for the further empirical parts of the paper:

2. Broader view: citizen voices in smart cities 2.1. Corporate smart cities vs. alternative smart cities Over the last few years, we have witnessed a spread of smart city projects around the world (Alizadeh, 2017) involving cities of all sizes (Kavta & Yadav, 2017; Watson, 2015) and diverse socio-economic status (Sanseverino, Sanseverino, Vaccaro, Macaione, & Anello, 2016). Smart city projects cover incredibly broad ranges of topics including but not limited to e-governance, smart transport, efficient urban services, and open data. Despite the heterogeneous smart city practices and projects worldwide, the critiques seem to be quite focused on what is labelled as ‘corporate smart cities’ (Hollands, 2015a; McNeill, 2016; Söderström, Paasche, & Klauser, 2014). From a critical perspective, it is argued that smart city has crystallized into an image of a technology-led urban utopia permeated with centrally controlled technological infrastructure (Albino, Berardi, & Dangelico, 2015; Niaros, 2016). In fact, in the corporate vision of smart cities, citizens are often seen as barriers in the race towards smartness; and that they need to be educated as to the benefits smartification can bring (McNeill, 2015; Vanolo, 2014).

2.2.1. Social media Social media is an umbrella term, for so many different online platforms, mainly introduced and gained momentum in the last decade, including but not limited to twitter, Facebook and Instagram; with the key feature of allowing users to connect. Social media is broad reaching and allows dispersed groups and individuals to connect and share or promote information relating to common interests, concerns, or causes (LaRiviere, Snider, Stromberg, & O'Meara, 2012; Minton, Lee, Orth, Chung-Hyun, & Lynn, 2012; Walther & Jang, 2012). Social media has played an important role in a number of civic uprisings around the word including but not limited to Arab Spring, the Occupy Movement, and recent presidential election campaigns in the US (Farro & Demirhisar, 2014; Gleason, 2013; Morozov, 2009). This has prompted a new line of scholarship focusing on the role of social media in enabling participation, creating collective voice, and facilitating socio-political 2

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2.3. Shortcomings: crowdsourcing in urban decision making processes

change (Comunello & Anzera, 2012; Kavada, 2015). Social media is participatory and interactive, which is also what separates it from the traditional forms of media (Fieseler & Fleck, 2013). The user-generated content on social media provides avenues for building bottom-up movements and empowering collective voices (Willems & Alizadeh, 2015). In contrast to these possibilities of empowerment, there is also growing skepticism around the quality of online voices, sheer size of distracting noises online, and legitimacy of citizen voice captured online (Ferrara, Varol, Davis, Menczer, & Flammini, 2016). Nevertheless, the complexity of the social media debate is, partially, due to its growing ability to provide an alternative voice (Fieseler & Fleck, 2013; Walther & Jang, 2012), with the capacity to complement the traditional public participatory approaches. Social media's dynamic nature allows for both bottom-up and top-down community involvement. From a bottom-up perspective, there is growing research on the use of community-led groups to organize and coordinate via social media in opposition to planning, policy, and manufacturing or development processes (Alizadeh, Farid, & Willems, 2018; Shav-Ami, 2013). In a top-down engagement perspective also, there is a growing line of literature (Afzalan & Evans-Cowley, 2015; Evans-Cowley, 2012) that argues planners can greatly utilize social media based opportunities to mobilize and organize citizens.

In principal, crowdsourcing has great potential to complement the traditional participatory urban planning approaches; promotes many elements of smart cities including open government; and can be used as an expansion of e-governance to we-governance by facilitating citizento-government support, citizen reporting, and citizen-government coproduction of cities (Castelnovo, 2016; Schmidthuber & Hilgers, 2017). Nevertheless, the problem is the scale of uptake of crowdsourcing in urban governments (Alizadeh, 2018; Berst, Enbysk, & Williams, 2014; Bertot, Gorham, Jaeger, Sarin, & Choi, 2014). Indeed, the small but growing number of crowdsourcing in urban governments mostly falls into the category of active crowdsourcing; as a special question is posed to the public (e.g. in times of emergency responses in disaster management (Poblet, García-Cuesta, & Casanovas, 2017)), or a new application/platform is introduced to reach the crowd (e.g. crowdsourcing of real-time data from the residents about the conditions of local roads (Harford, 2014)). This paper, motivated by the gap in the practice of crowdsourcing, takes a step towards using passive crowdsourcing to inform local urban planning processes. Below parts describe the ins and outs of the study behind this paper; and unfold some of the preliminary findings. 3. Our study This paper is part of the alternative smart city vision, discussed earlier in the paper, and puts citizens' voices at the center of smart city thinking. Indeed, it is based on a project funded in the first round of Smart Cities and Suburbs Program initiated by the Australian Government in 2017. The overall project involves collecting citizen voices from a range of online sources, including social media; and feeding them back to the public domain and also to the local governments by developing two online dashboards (one for the public, and one for local governments). Due to word limits, we will not be able to cover the full range of activities in the overall project in this paper. Below outlines the context for the paper, the scope, and the methods adopted for data collection and analysis. The discussions conclude by explicitly showing how this study aligns with the core elements of alternative smart city vision, described earlier in the paper:

2.2.2. Crowdsourcing Howe (2006) first coined the term crowdsourcing in a Wired Magazine article as “the act of a company or institution taking a task once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call”. Since then there have been many attempts to revise and redefine crowdsourcing based on the diversity of its practices (Estellés-Arolas & GonzálezLadrón-De-Guevara, 2012; Zhao & Zhu, 2014). Some of the latest revisions have been proposed in response to the emergent crowdsourcing based on the eminence of social media (Kietzmann, 2017). However, the most significant evolution of crowdsourcing concept stems from a shift from the original ‘task-oriented’ approach to what can be described as ‘crowdsourcing of opinions’ (Alizadeh, 2018; Noveck, 2015). In this second approach, crowdsourcing is no longer about getting a certain task done by the help of the crowd. Instead, crowdsourcing of opinions is used to gauge opinions, ideas, or perceptions of the public in different forms of polling, sentiment analysis, and opinion mining. Sentiment analysis uses language processing and machine learning to identify which topics different groups talk and care about the most. Social media in general, and twitter, in particular, are rich sources of opinions; and have been used in crowdsourcing of opinions. There are, indeed, numerous examples of companies using crowdsourcing of opinions - via social media–in their marketing efforts (Dowson & Bynghal, 2011; Willems & Alizadeh, 2015). Crowdsourcing of opinions, in turn, then is categorized in two broad categories of active and passive. In terms of the difference between active and passive crowdsourcing, Loukis and Charalabidis (2015) argue that the active crowdsourcing of opinions is more like mainstream private sector crowdsourcing which actively stimulates discussions and content generation by citizens on specific topics. While passive crowdsourcing approach is more compatible for the public sector; it passively collects information, knowledge, opinions and ideas concerning hot topics of the day created by citizens without any initiation, stimulation or moderation from government postings (Charalabidis, Loukis, Androutsopoulou, Karkaletsis, & Triantafillou, 2014; Loukis & Charalabidis, 2015). Social Media Monitoring (SMM), as a systematic continuous observation and analysis of the data already available and mostly untapped, is the main source of passive crowdsourcing in the public sector (Loukis, Charalabidis, & Androutsopoulou, 2017).

3.1. The context: smart cities plan The study behind the paper should be seen in the context of the broader shift within the Australian smart city marketplace, whereby the federal government has created a policy platform, ‘Smart Cities Plan’ (Commonwealth of Australia, 2018b) aiming to increase the productivity, livability and sustainability of our cities through the application of three pillars which include smart technology, policy, and investment. Through the program, Smart Cities and Suburbs, the federal government ‘kick started’ the investment in ‘smart initiatives’ by incenting collaborations between industry, academia and local government. After a competitive process, 49 projects were selected and supported to the value of 27.7 million Australian dollars, of which this study is one. Interestingly, of the 49 projects 13 involved a core element that related to using smart technologies and/or the use of real time data to enhance community engagement, for example 3D modeling virtual and augmented reality; the other 36 funded projects related to the deployment of smart technologies to improve service efficiency and the collection and analysis of real time data (including mobility and pedestrian data) to help local government decision making (Commonwealth of Australia, 2018a). The study behind this paper was one of the few that primarily considered passive crowdsourcing of citizen's opinion to inform government decision-making. Topic distribution of smart city initiatives, funded by the federal government, indicates that capturing the diversity of citizen voices is a critical, yet fledgling smart initiative priority, with a long way to go. 3

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community. For the Parramatta and Five Dock projects, all possible combinations of “Five Dock” and “Parramatta Road” were used, along with “Sydney”. This was to ensure that as far as possible only tweets relevant to the local areas were retrieved (for example, Five Dock could be a place elsewhere in the world, etc.). By all combinations, we mean alternatives such as #fivedock, Five Dock, #parramatta road, #parramattaroad, #parramattard etc. The aim was to capture as far as possible a full set of linguistic variations. It should be noted that while full effort was made to capture the full set of attributes associated with each tweet, most users have their geographic locations switched off or hidden, in which case, it was not possible to capture geographic information. Discarding the number of tweets without geographic information was leading to an extremely sparse data set. Other tweet attributes included the user metadata associated with each tweet. Repeated tweets were removed, and only unique tweets were preserved, though a record and count was kept of what was being removed. Retweets that appeared genuine were not removed. Further, any tweets that were filled with random data and appeared to be sourced from bots were also removed. This part of the processing made sure that a maximum amount of meaningful information was being captured.

Shifts are afoot, with a growing number of local governments being willing to explore alternative ways to engage with citizens; and augment traditional community engagement practices such as face-to-face consultation forums with technologically-enabled citizen engagement. Moreover, within the realm of State Government, the government of New South Wales has passed amendments to the Environmental Planning Assessment Act 1979 (NSW Government, 2018) requiring local and state planning authorities to develop and implement a community participation plan (CPPs) to increase the participation of community members in planning decisions to improve planning outcomes. 3.2. Scope In this paper, we focus on the work conducted in collaboration with one local government partner, City of Canada Bay, as part of the broader project (funded by the federal government). The City of Canada Bay is a local government area in the Inner West of Sydney, New South Wales, Australia. The city covers an area of 19.82 km2 and as at the 2016 census had a population of 88,015. Following consultation with the City of Canada Bay and preliminary data analysis two pilot projects were selected within the local government area. In doing so, consideration was given to the: 1. Scale and impact of the project; What was the financial investment, project duration, physical scale and potential disruption to the built environment and therefore citizen use? 2. Diversity in type and location of project; A master-planned development or major infrastructure? Inner city or peri-urban fringe? 3. Role of governments in the urban project; Who is the lead proponent? Local government or state government? 4. Viability of data; Is there sufficient volume of data for analysis? Is the data providing meaningful insights to urban issues?

3.3.2. Twitter: data processing and cleaning The quality of Twitter data depends on multiple factors: primarily, the query used and timing. For example, the query “five dock” sometimes returns incorrect results regarding boat docks, or for some content that may not be related to Sydney at all. Timing is another factor as certain events may cause a burstiness of tweets, followed by spans of silence. For example, “Parramatta road” would become particularly active during events of traffic congestion or accidents. The fluctuating popularity of topics is particularly prevalent with Twitter due to its “trending topics” feature. For this paper, we have collected tweets over a period of 7 years. Table 2 shows counts of the numbers of tweets and the time frames for which tweet data was captured. As is evident, the volume of information and flow of information over time is relatively low. Yet, surprisingly most of the tweet content was very meaningful (further information on this follows). Below Table 2, also shows a snapshot of 7 days in order to provide an idea of how many relevant tweets are made in a week, though, this can vary depending on the burstiness of event related tweets. For example, in this particular week, the number of tweets captured is quite high, whereas in some other weeks, particularly low activity can be seen.

The selected urban projects, at the core of this paper, are introduced in Table 1. 3.3. Methods 3.3.1. Data acquisition In the first phase, we mined Twitter feeds for targeted data on each of the projects discussed above. Meetings and informal interviews were conducted by the team members with council representatives, who outlined the key pieces of information and background described above for each project. Using this as the basis, and a pilot study of Twitter streams, the team developed a set of hashtags and keywords related to each project. It should be noted here that using some of these keywords and hashtags returned data that may not be concerned directly to the projects, but could nonetheless reveal interesting and relevant information. For example, “five dock” could bring out the café culture activities, biking, and community meeting activities that seem common in the Five Dock area in Sydney, which in itself could be a sign of an active

3.3.3. Sentiment analysis Tweets were then analyzed to assess their overall sentiment, either positive or negative. We first removed meaningless words such as Twitter handles, URLs and stop-words. The remaining, meaningful words were then individually assigned a sentiment score. Words such as “happy”, “good” and “sun” were given a positive score while words such as “angry”, “traffic” or “lost” were given negative scores. The

Table 1 Summary of the urban projects – located in the city of Canada Bay. Project name

Description

Parramatta Rd transformation project

The Parramatta Road corridor is described as an important transport and movement route for people who live, work and travel in the area; is characterized by chronic traffic congestion, noise and as the connector of Sydney CBD to Parramatta, is a priority area for the long term growth of Sydney. Three renewal areas are identified within the City of Canada Bay LGA, including Homebush, Burwood and Kings Bay Precincts. These precincts are expected to provide an additional 17,000 dwellings to house approximately 36,100 people and provide up to 19,600 new jobs (Landcom, 2018). The Five Dock Town Centre Urban Urban Renewal project sets out a vision for Five Dock to ensure that the centre continues to provide a strong focus for the community, is a better place to live and work, creates improved opportunities for investment, is easy to get around and provides an enhanced built environment (City of Canada Bay, 2018).

Five Dock urban renewal

4

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Table 2 Tweet counts. Project Parramatta Rd Five Dock

13/08/18–20/08/18 (snapshot of 7 days)

Complete unique tweet repository

Complete tweet repository (with repeated tweets)

68 143

2015 2392

2337 3060

4. Preliminary findings

sentiment analysis was performed using a standard Python based library that uses a Naïve Bayes and bag-of-words approach. These individual scores were combined using the Python Pattern library's PatternAnalyzer to give a combined sentiment score between −1 and 1. An additional feature of the Python library is that it provides a measure of subjectivity versus objectivity of information: if a tweet contains factual information, it is classified as less subjective, whereas tweets with more opinion based information are rated more on the subjectivity score. It is to be noted here that given any algorithmic limitations (too technical and out of scope of discussion for this paper), the results would not be perfect, though they are reliable.

Figs. 1 to 6 show the preliminary results of the analysis. Figs. 1 and 2 show the results of the sentiment analysis for Parramatta Road (2015 tweets) and Five Dock (2392 tweets), respectively. Each dot represents a tweet, the x-axis plots the subjectivity of the tweet, and the y-axis measures the polarity (or the positive-negative sentiment). The more green the color of a dot, the more positive the tweet, and the more red the color of a dot, the more negative the tweet. It is interesting to observe the spread of the data across both urban projects (Parramatta Rd., and Five Dock). It is important to remember that, here, data refers to the diversity of topics discussed by the citizens in their tweets about the two urban projects. First, there is an equal distribution of negative vs. positive opinions expressed in the tweets. This is perhaps surprising; as there is a line of literature which argues that people mostly use Twitter (or social media in general) to vent out, to complain, and basically to be negative about urban issues (Resch, Summa, Zeile, & Strube, 2016). Our sentiment analysis, for both Parramatta Rd. and Five Dock, however shows a fair distribution of opinions. More interestingly, the less subjective the information shared via each tweet, the lower the spread or dispersion of the polarity. To the contrary, the higher the subjectivity of the information, the higher also the dispersion of the polarity. This shows that more subjective information has higher extremes of emotional expression of positivity or negativity built into it. It is also interesting to observe from Figs. 1 and 2 that the overall distribution “shapes” for both the Parramatta Road and the Five Dock tweets are quite similar. Regarding the content of the tweets, most of the positive tweets focused on good personal experiences, social events, or at times marketing related information. Most of the negative tweets focused on topics such as traffic and congestion, poor citizen or driver behaviors, or complaints against government or poor infrastructure (e.g. road conditions, telecommunication failure). Table 4 provides a few examples of positively and negatively classified tweets for the Parramatta Road and Five Dock: Figs. 3 and 4 show the results of the cluster analysis for Parramatta Road and Five Dock. The size of the word corresponds to the highest frequency words of each cluster. Fig. 3 shows word clouds for the top 3 topic clusters identified for Parramatta Road. The clusters can be broadly classified as: (a) delayed traffic, (b) accidents and breakdowns, and (c) planning and government related tweets. Fig. 4 shows word clouds for the top 3 topic clusters identified for Five Dock. The clusters can be broadly classified as (a) Five Dock popular culture, (b) games and sports events, and (c) traffic delays and congestions. In terms of the content, each cluster identified is rich and meaningful as everyday citizens share their observations, frustration, and happy moments. For instance, a broad range of topic areas are discussed with direct relevance to local government decision making as part of the planning and government related tweets (cluster (c) for Parramatta Rd.): including but not limited to street parking, new constructions' noise pollution, public transport, and housing. Tweet examples follow:

3.3.4. Clustering analysis The next stage in analyzing the data was clustering the tweets, that is, putting the tweets together in clustering that brought out information on a particular recurrent topic that was getting repeated attention. We used a technique called Latent Semantic Analysis (LSA), similar to graph clustering (Sarkar & Dong, 2011). First, we created a term-bydocument (TDM) matrix. As mentioned above in the case of sentiment analysis, all stop words were already removed from the tweets, and all meaningful non-stop words extracted. Each of these unique m words formed a row in the TDM matrix, i = 1, 2, …m. Each column formed one of the n tweets, j = 1, 2, … n. Each matrix entry is a record of the number of times the word i occurs in tweet j. We then performed the Singular Value Decomposition based algorithm to cluster the data (Sarkar & Dong, 2011). This algorithm extracts a lower-dimensional representation for the high-dimensional tweet data. In this lower-dimensional representation, words and tweets that frequently co-occur with each other lie “close to each other” in this abstract mathematical space. The extracted clusters were then examined by counting the top words in each cluster in order to identify the topics. 3.3.5. Sentiment analysis within each cluster In the final stage of analysis, the results from the cluster analysis and the sentiment analysis were superimposed, which allowed us to track and monitor sentiments by clusters and topics. For example, in clusters such as “traffic delays”, “congestions” or “accidents” the overwhelming number of negative sentiments was visible, but that even within these topics there were a substantial number of positive sentiments which was a surprising and novel finding. In topics such as “sports events”, positive sentiments were more visible, and more expected. Further, the volume of tweets within each cluster provided an indicator for how important a particular topic or cluster is for the public, and combined with the sentiment analysis, presented a breakdown of the points within this topic or cluster that are perceived positively, negatively, or neutrally. 3.4. Alignment with alternative smart city vision Before jumping into any discussion about the results of the study, it is important to show how our study aligns with and reinforces the alternative smart city vision (discussed earlier in the paper in Section 2.1). Table 3 shows how our study accounts for the core elements of alternative smart city vision (Alizadeh, 2018). As foreshadowed in the earlier discussions, finding common ground among smart urban projects, which display an alternative to the corporate vision of smart cities, empowers a move from alternative to mainstream, to secure the social sustainability of smart initiatives.

What's wrong with people? This is Parramatta Road, not a quiet Annandale back street!! They should address the parking issues. Somethings needs to be done to deal with noise emanating from the building sites in Parramatta, but sheesh… if you can't build there, where can you build? Councils across the country are spending more than 1bn putting 5

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Table 3 Alignment to core elements of alternative smart city vision. Element

Observations

Citizen engagement beyond the simple delivery of services.

The consideration of citizen opinion data during the study is indiscriminate; meaning the opinions collected were what the citizens were thinking about or reacting to at the time, regardless of specific services of government. The study is based on the premise that the opinions of the citizens are paramount, and therefore were collected using unprompted and unbiased methods; the analysis described above bringing life to the emerging sentiment and topics of interest. While public-private partnerships are not primary in this study, the outcome is to enhance citizen agency through the development of the internal capacity of local and state government to directly collect and listen to citizen opinion thereby optimizing participatory governance. The study actively seeks to crowdsource citizen opinions and priorities to inform urban problems and solution development.

A democratic bottom up approach: to promote participatory urban technologies, greater social inclusion, and a substantial shift in power from corporations to ordinary people and their communities. Reliance on dynamic public-private partnership: with an emphasis on participatory governance rather than entrepreneurial one.

A tendency to identifying the urban problem first, and only then reaching out for the relevant technological solution: with emphasis on the capacities of each city, and its distinct cultures, histories, and political economies. Is Associated with the free software and open access movement

The study is aligned with providing free software to build citizen and local government capacity. The study is exploratory.

Is in the preliminary phase: far from being mature; and mainly exist in seed form

tweets. This is not unexpected, and relates to the intensity and complexity of issues experienced in Parramatta Road versus Five Dock which is a lower-profile local project. Having said that, there is still specific and detailed information captured among Five Dock tweets that could be valuable – especially if presented and responded to in ‘realtime’ or ‘near-real-time’ basis. Below is an example, which reports a potential break down in the road system: Some buses travelling through Five Dock and Haberfield are delayed up to 20 min due to heavy traffic on Parramatta Rd and Ramsay Rd!! In order to take the analysis to the next level, Figs. 5 and 6 represent a sentiment analysis of different cluster (topic areas) identified in the Parramatta Road and Five Dock tweets. In other words these figures provide an opportunity to better understand the intensity and direction of public conversations in each topic area identified via cluster analysis. A few interesting observations emerge. In each case, the blue dots are the tweets captured for the case study project (Parramatta Rd. or Five dock), but outside the specific cluster. The density of the blue dots in relationship to the other tweets also shows the volume of each topic area (cluster) versus the overall tweets captured. Fig. 5 shows the sentiment analysis for the Parramatta Road tweets in the three identified topic areas of (a) delayed traffic, (b) accidents and break downs, and (c) planning and government related tweets. Topic b ‘accidents and break downs’, is understandably more negative than the other two topic areas. Having said this, the level of positivity, even in such a dire topic area, comes as a surprise. Numerous tweets, such as the below example, suggest that people share the good news online when the delay in traffic is sorted:

Fig. 1. Sentiment analysis for the Parramatta Road tweets.

CAMPERDOWN: All lanes are now open on Parramatta Road at Layton Street, after an earlier car crash. Traffic is very light.

people in temporary accommodation because we don't have enough houses and I'm sure there's nothing better we could spend that money on.

Nevertheless, the most complex, and informative topic area identified in the Parramatta Road tweets is perhaps the ‘planning and government related’ cluster. A range of diverse topics including housing affordability, development rate, alternative modes of transport are discussed. Most interestingly, some interesting planning questions, or alternative proposals are also pointed out. Examples include:

The worst part of Sydney is definitely the overabundance of 413 and 431 buses. I'm just trying to get down Parramatta road.

Parramatta Road or Parramatta River? Should more commuters use the river? Enjoying alt trip. #Sydney #parra #ferry.

Very cool that a 40 min bus ride can take me between two European capital cities, while it takes 40 mins just to get down f**** Parramatta road.

Great news that the Parramatta Road Urban Transformation Strategy includes an affordable housing target @UrbanGrowthNSW @NSWPlanning.

There is a broader diversity of topic discussed in the Parramatta Road tweets in comparison to the patterns observed in the Five Dock

Parramatta Road housing reduction and proposed levy could slow down development.

Fig. 2. Sentiment analysis for the Five Dock tweets.

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Fig. 3. Cluster analysis for the Parramatta Road Tweets (a) Delayed traffic (259 tweets, ~13% of the 2015 tweets), (b) accidents and break downs (1058 tweets, ~53% of the 2015 tweets), and (c) planning and government related tweets (280 tweets, ~14% of the 2015 tweets).

Fig. 4. Cluster analysis for the Five Dock Tweets (a) Five Dock popular culture (1007 tweets, ~42% of the 2392 tweets), (b) games and sports events (57 tweets, ~2% of the 2392 tweets), and (c) traffic delays and congestion (1328 tweets, ~56% of the 2392 tweets).

It seems like ‘planning and government related’ cluster is the one to look for bold planning ideas in addition to the announcement of latest planning news such as:

may reveal more information on the triggers of positive or negative reactions from citizens, or explain how public opinions shift over time about a certain urban project or issue.

Latest plan for Parramatta Road Corridor announced yesterday $31b transformation #TomorrowsSydney #nswpol @NSWPlanning.

5. Conclusion: what we learned and where to go from here

Fig. 6 shows sentiment analysis for the Five Dock tweets in three identified topic areas of (a) popular culture, (b) games and sports events, and (c) traffic delays and congestion. Higher volume of the topics b and c represents their level of importance. More interestingly, there seems to be a lot of overlap between these two topic areas. This is quite informative in terms of planning responses, as it suggests that the utmost majority of tweets about traffic delays in Five Dock happen during the major sport events. An example includes:

To date we have used machine learning methods to extract overall sentiments on the urban projects at the core of our study. While sentiment analysis is able to provide an overall idea of positive or negative opinions on the projects, a deeper look at the data for its content was required. Thus, we used a second set of machine learning methods (sentiment analysis and cluster analysis) to extract primary latent topics in the data. These reveal, along with the positive and negative sentiments, the areas of primary concern for the public. In doing so, we have provided an opportunity to apply some the existing and peer reviewed data mining and machine learning algorithms to a real world problem around the challenge of collecting citizen voices on local planning projects. The analysis reveals that the rate of participation is low, but meaningful. A very small portion of population have so far participated in the online conversations on the urban projects at the core of our study. But those who do participate often leave meaningful observations that have the capacity to inform the decision-making process. In other words, our learnings resonates with the questions put forward at the beginning of the paper. As they represent the potential of the already existing and peer reviewed data mining and machine learning algorithms to collect citizen voices online; and feed them into local government decision making process. In this representation, is our paper's main contribution: to propose a way forward, to enable public engagement in smart city debates and decision making – especially at

Five Dock: A 26-year-old male driver was taken to hospital following a late night crash on Kings Road. #rougbyleague #drinkdriving. Interestingly, the level of detail included in some of the tweets is also valuable if assessed in real-time or near-real-time bases. Below is an example. FIVE DOCK: A car's broken down on Parramatta Rd at Taylor St, closing 1 of 3 eastbound lanes. Heavy traffic, allow extra travel time. In sum, the preliminary findings show great potential for further investigations of topic areas discussed by citizens in their tweets and to better understand the topics of concern that require responses from government. Especially real-time analysis of tweets has the potential to inform the government on the burning issues in need of immediate action. Moreover, we also see potential for longitudinal analysis as it 7

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Fig. 5. Sentiment analysis for the Parramatta Road tweets in each cluster (aupper row) delayed traffic, (b- middle row) Accidents and break downs, and (clower row) Planning and government related tweets. In each case, the blue dots are the tweets identified outside the particular cluster, and the position on the polarity axis is a measure of positivity or negativity. (The red and green dots are other tweets in the cluster). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6. Sentiment analysis for the Five Dock tweets in each cluster - (a – upper row) Five Dock popular culture, (b – middle row) Games and sports events, and (c) Traffic delays and congestion (lower row). In each case, the blue dots are the tweets identified outside the particular cluster, and the position on the polarity axis is a measure of positivity or negativity. (The red and green dots are other tweets in the cluster). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

the local government level. We acknowledge that the analysis included in this paper falls short of the all-to-all characteristic of social media communication, as it is restricted to a one-way communication which is about “listening” to the citizen voice. The future steps of the study will move into a direction of facilitating an actual dialogue among multiple stakeholders. Our future steps for the study are three folded: First, we are in the process of advancing our data analytics. The next step is to focus on real-time data; to better inform local governments on the immediate actions required. This, combined with the longitudinal analysis of data, will help us identify underlying factors that may

trigger citizen's response to certain urban projects/topics; and has the potential to promote responsive local government practices including both listening to citizen and (re)activing upon them. Second, we are in the final stages of dashboards design to enable an all-to-all communication as we produce two dashboards. One citizen dashboard is to feed our analysis of citizen voices and concerns back to the citizens; and the other local government dashboard is to inform local government decision making processes and outcomes – providing an opportunity for them to be responsive and close the communication loop. The dashboards design is well in progress, and we are in final 8

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Table 4 Examples of positively and negatively classified tweets. Tweets

Parramatta Road

Five Dock

Positively classified

“Best I've seen that stretch of Parramatta road look” “The joy of Parramatta Road this evening” “NOW is the perfect time to invest in Homebush and become part of a new growing community” “There is a bad pothole round a manhole/access cover on #ParramattaRoad,westbound,ouside #VictoriaPark just b4 SydneyUniversity. Others closer” “That's so very terrible, @davidtickle_. I've always found Parramatta Road to be a traffic funnel, not good for business or people!”

“Looking forward to #Ferragosto2017 today in #fivedock Come on #innerwesties it's a great day for food, sun and fun!” “Good morning #fivedock Happy Tuesday twitterverse! #happydays #lovinglife #canadabay #innerwestisbest” “Hey @Telstra. Is there something wrong with the broadband service in Five Dock 2046? The service has been slow for 3 days.” “#innerwest #sydney be careful of your children! Kidnapping attempt Belfield burwood Five Dock Drummoyne Haberfield”

Negatively classified

consultation stage with the local government partners to assure the dashboards are responsive of the local government's expectation and needs. At the same time the process to register the IP rights of the dashboards is in progress (we will be able to discuss the details of dashboard designs as soon as the IP process is finalized). The functioning dashboards will provide further opportunities to test the reliability and quality of social media data; and to explore the role that they can play as additional data sources for capturing public opinions on urban projects in ‘real time’ or ‘near-real time’. Last but definitely not least, we are hoping to expand our study to a larger network of local governments in different contexts. If anything, our preliminary findings have presented an exciting potential for passive crowdsourcing via social media platform to enhance our understanding of what matters to citizens in terms of urban questions. Building a larger network of local governments has the potential to enable responsive urban decision making based on an informed understanding of citizens' concerns and priorities. This will then enable participatory planning which is socially responsible and respectful to the diversity of citizen voices captured online.

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