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Original Article | Open Access | Asian J. Soc. Sci. Leg. Stud., 202; 6(1), 1-18 | doi: 10.34104/ajssls.024.01018

Urban Themes Constructed in the Persian Twitter

Maryam Peimani * Mail Img ,
Abdolhossein Kalantari Mail Img

Abstract

This study was conducted to investigate Persian tweets with urban content on Twitter. The main attention of this research is 22 city-related words applied to the tweets. The method of this research is to mine big data extracted from Twitter over the course of four weeks. The basis for analyzing is the text of tweets. The applied concepts and theories are borrowed from Habermass public sphere theory, Lefebvres spatial dialectic, and Manuel Castles space of flows. The results obtained from the analysis of tweets suggest that Persian Twitter users emphasize the central cities of Tehran and Isfahan when discussing the city. The space of flow theory is also visible without it contradicting the spatial dialectic of Lefebvre due to the spatial structure of Twitter, as users go back and forth between the urban space and the online space in an attempt to have a conversation with each other about urban issues. In Persian Twitter without historical and spatial logic, users write about the city and communicate with each other. Finally, despite the centrism mentality in Persian Twitter, it has challenged the possibility of forming a dialogue space for citizens.

INTRODUCTION

With the structural transformation of the public sphere in the view of the Habermas, cities became public do-main. urbanization means the separation of social life into two spheres, public and private, in which there is a mutual relationship between these two spheres the diminution of the private sphere to the nuclear family made it possible for individuals to enter the public sphere without the support of an institutional private sphere in their leisure time or at the time of employ-ment this segregation attempts to fill the void of the bourgeois public sphere (Habermas, 1999). In fact, modern urbanization shapes the new structure of the public sphere. This public sphere consists of  govern-ment intervention and the private capitalist who control it or operate in it. Todays cities and the metropolises is the arena of activity for the general public. Those who travel in it every day and, accor-ding to Lefebvre, form various spatial actions in it. These actions are by the mass of the people or the establishment of law and power by the government and quasi-governmental institutions. Representations of urban spaces are in the hands of engineers and employers who carry out urban modifications and the constructions according to the rules and profitability of capital. Roads, public buildings or private muni-cipal elements, parks, warehouses and shops, etc. All are in the hands of state or non-state power. Ulti-mately, only in them do citizens arrange their daily actions or, in their art and writing, blend spaces of the representation with imagination and produce artistic and writing productions. The social construction of reality takes place in the everyday affairs. Language shows the coordinates of the lives of individuals in society and this life is replete with meaningful topics. What is presented to a person in everyday life here and now‎ is the reality of his consciousness, in this way each person experiences everyday life based on his place and time, and his attention to the world around him is what it is to do anyway. So, the objec-tive reality of society and its persistence in everyday life constitute each persons perception of the world around him and this perception in the social context makes‎ play a role. And these realities are all con-structed and dealt with in society (Berger, 1399). Now we are faced with the fact that in todays cities, in the transformation of the structure of the public sphere, social reality is built in such a way that not all inter-actions and actions of individuals take place in the geographical space of the city, but many communi-cations and actions of citizens in the Internet arena. 

Today, many people use the Internet to procure con-sumable items, to carry out office and leisure acti-vities, and to conduct friendly gatherings. This new space, despite the existence of social networks, has allowed the range of the citizen communication and interactions to go beyond the neighborhood level, with anyone anywhere expressing their thoughts on urban topics and the urban public sphere in it and others who may never have met having interactions with him. It is in this way that the importance of refining Twitters space as a social network becomes clear. 

We need to see what the citizens who show up in the cities every day say about cities? Watch the high volume of Persian Twitter productions about the city, to know what points do we have about the cities of our lives when the participation of citizens is very low, what are their contributions on Twitter from time to time? Social interactions in the city, as Mahdavian mentioned in his dissertation, in the city of Tehran have caused the possibility of interactions in the city to be greatly reduced due to the commodification of the urban space. (Mahdavian, 2017) In the context that the possibility of urban interactions in geogra-phical space seems difficult & sometimes impossible, the existence of Internet space in social networks is a kind of the alternative to building urban public space.‎ That is why it is important to understand how and what communication is about in this space, even if this space is not a mirror of the whole social facade, because not all citizens are present in it, but again, due to the many interactions in this space, one can observe the space of flows in it.

Representation spaces and the conversations that form around them are important to our research since by using features such as hashtags and how many ret-weets from a tweet, this space will be a dominant space among individuals, and this makes it possible to have a magnified image of this space because in a public space a dominant discourse is important. By contrast, those tweets that dont get much reaction are emblematic of marginal discourse about the city. On the other hand, representations of space in the city cause citizens to have various space actions, and these actions are sometimes suppressed due to laws or the social contracts. For example, one of the space acti-vities is cycling, which is carried out in the streets and alleys of the city, but this same space action may be difficult because of the laws in Iran for women and they cannot use bicycles in the city. When this space action is banned, there is no possibility to talk, protest and complain about it in a geographical location but Twitter allows citizens to continue using their tweets. Have a space action on Twitter. Because of its avail-ability in all places, the internet has this privilege over other public spaces that do not require city-level relocation to establish urban interactions, but it has the same point on the other. Citizens need to relocate to gain access to geographic locations and access urban amenities. 

Therefore, on Twitter, it is not spatial action, but the sound of actions. Different voices in Persian Twitter can tell us if there is a conversation and discussion about a space action that has taken place in a parti-cular geographical location, what representations of space do these conversations have? How do they des-cribe it, and what do they feel about it?  The entry of the masses into the public sphere caused more govern-ment involvement in the public sphere, whereas the sphere was supposed to be for distance from its power and control. Laws are passed in parliament and then enforced by the government without it any longer caring what the public thinks. Only at the time of polls is propaganda techniques used to attract citizens votes by parties. Public opinion in this position means the political demands being said by parliament and the parties. For this reason, when public opinion is refined by opinion polling bases, it must be considered that public opinion is reflected in the form of legal reality present in the effect of socialization and social control. On this basis, the official opinion that is highlighted in the media and in advertising is reflected in the public, and informal thoughts become entangled in the islands of peoples personal lives (Habermas, 1399; Fairooz et al., 2023).

A political society with a government and the legal institutions must rely on the rule of the law. Citizens should also be able to separate objective and sub-jective attitudes in their everyday actions and thus in communicative action be able to use the critical capa-city of speech because in a place of the lifeworld where social connectedness is dimmed, media and propaganda try to intervene in it by reproducing pre-conceptions and make systemic linkage with tech-nology sit in its place. In this sense, the application of the concept of the public sphere is, in fact, the trans-formative public sphere, extended in all dimensions of social life and realized through the communicative action between the actors of society. Its the public sphere in the tug of state power and the power of the rich, which is why its important to find different informal voices in it (Habermas, 1392, 524-525). 

Regarding the right to the city, Harvey argues that the right to the city should be taken to mean the right to rule over the entire process of urbanism, which was increasingly pervading rural areas through phenomena ranging from agricultural business to second homes and rural tourism. He goes on to state that urban social movements, which are numerous throughout the world, are in fact often unrelated to each other. What should be their desire if these were to be joined in some way: greater democratization control over the production and use of surpluses because the establish-ment of the city using the surplus has been made possible (Harvi, 1392, 53). The space of flows is the material organization of social functioning that have temporal subscripts and act through flows (ibid., 477). Flows are a series of interactions between actors that are not physically connected to each other, and the space of flows is the material support of the dominant functions in the information society; the first layer is electronic stimuli, this layer is a form of space like city. The second layer is nodes and axes, which deter-mines the main data base. The third layer refers to the spatial organization of the dominant managerial elite. People are local but elites are cosmopolitan, which is why the more a social organization rests on non-historical currents, the logic of world power escapes the social shackles of local communities. Elites are also indispensable to distinguish themselves from the mass of people who draw the line between insider and non-insider. Stream-space architecture is a post-modern architecture that pretends to say nothing and silence is its message (Castelles, 1389, 477-488). 

An important question about these spaces is who has the right to live in the image that exists of cities? Social groups compete with each other for access to important city outposts, and city power means the power to exert a particular image of the city. The power of the image they try to impose by preferring one representation to another representation. Home-less people and addicts, for example, are driven from areas of the city where they have a greater presence. These dominant images are the result of the marketing of cities and are usually oriented to the power struc-ture. Thats why theyre more interested in attracting imaginary consumers. The two most important images in this theme are promenades and passageways (Castells, 239-307). Cyberspace is an urban space that no longer takes root in a specific location on Earth, limitations on connectivity and bandwidths have more impact on the formation of these cities than access-ibility and land value. Their operations are carried out in an asynchronous manner, and the subjects have occupied these cities, which are undeveloped and the separate incarnations of each other and exist in the form of collections of users and pseudonyms. The locations and headquarters of these cities are built virtually and with software not with physical mate-rials and also instead of city passes, computer logic links connect these places. From this perspective, urban design is as related to computer programming as it is to the‎ composition of the public spaces (ibid., 452). A place is a site which is self-sufficient by the form of function and meaning within the boundaries of physical proximity. For example, a city always remains a city, but different people commute in it. But the atmosphere of flows implies a rapid and wide-spread arrival and departure of people in a place that is not subject to local rules. People still live in places but function and power are achieved through the space of streams. Space of flows tries to impose its non-historical, networked logic on fragmented places (ibid, 494). 

After the Egyptian revolution, the question arises in abundance as to whether the role of Facebook and twitter is decisive in this regard? After the Egyptian revolution, there were various headlines in news-papers and magazines calling this revolution the Face-book and twitter revolution. But then doubts were raised such as whether there was no possibility of a popular uprising without these social networks? People who were present in Tahrir Square were asked how much face-to-face communication and how much they used mobile communications to coordinate the dissemination of‎ information. They concluded that only 13% of people have benefited from contacts such as Twitter and 98% of people from face-to-face communication. They have used the face-to-face com-munication as a source of information and coordi-nation. So, this revolution wasnt really an internet revolution but the possibilities that mass and social media gave it, made it take on a different show from past upstarts. 

A study was conducted on the Wall Street movement that occurred shortly after in the United States, and yet again, even though people had 30 percent more Internet access in the United States than Egypt, the importance of face-to-face communication was much greater. Thats why the Twitter revolution and the Twitter uprising is just a myth that is being debunked more and more by governments every day. But it should not be forgotten that in this current atmosphere and with the same possibilities, it has been possible to have different activism and to create a space in which to engage in dialogue at certain points in time, such as the release of the WikiLeaks documents and the disclosure of information by Snowden (Fush, 2014, 181-200).

METHODOLOGY

to collect data at the right time, the one-month interval of the protests of farmers in Isfahan was set as a time criterion (November 14 to December 12, 2021) in order to have a valid time measure, i.e., an urban crisis in one of the cities of Iran, and based on this, we can obtain spatial action in meters and measure the texts generated on Twitter Bring. Protests by Isfahani farmers started in the city from the beginning of the year 1400. After the end of the summer and with the beginning of the autumn season there were protests of these people sporadically throughout the city. For example, a rally was formed in front of the Isfahan Regional Water Company, but again these protests continued. From the beginning of November, farmers gathered and sat on the dry bed of the Zayande Rood. The continued sit-ins and gatherings of these people led to other people joining the rally until the gathering ended on Friday, December 5, 1400 with the inter-vention of law enforcement (Quoted from the Farsi Wikipedia). We considered the month-long interval of these protests as directly marking the occupation of urban public space in order to achieve its demands, in accordance with the interval of extracting our data from the Persian Twitter. The process of text mining involves a variety of activities that extract information from unstructured text data. There are many different methods for text mining. Among these are infor-mation extraction, method based on used phrases, and text grouping. One way to extract information from the context of its keywords. This method briefly unveils the content of the text and gives us a general theme of the content. (monkeylearn.com/text-mining/) In a study of users with suicidal content on Twitter, words related to suicide were extracted from various suicide forums, and after refining it for several days on Twitter these words were presented to psychology professionals and a few related words were identified at the end became. It was then worked on extracting tweets associated with that word in 6 weeks. Classi-fication of urban public spheres was used to obtain valid words in the urban sphere. According to Kamran Zakavat in his article, urban public spaces are located on several floors, those that take place in urban spaces such as streets and alleys and squares, or ecological spaces, which include tourism and health, commercial and service spaces. On the other hand, because urban public space is primarily a space where people inter-act socially and in which there are urban and com-munity components, urban public transport is also an integral part of the urban public space (Shojaei, 1394). Similarly, the list of words associated with the city was extracted to compile the desired data on Twitter as follows: City

1) local public places: Street, Park, Mosque, Vali-easr (street) 

2) Urban transport: Bus, Subway, Snapp (like Uber in Iran), Tapsi (again another company like Uber), Bicycle  

3) Local and Urban Ecological Areas: the City Council, Stadium, Municipality, Gym, Store

4) Urban Problems Occurring in Public Places: Water, peddler, Working Children, the Disabled People, Elderly, Quarantine, Housing, Neigh-boring, Rent 

5) And the specific names of the cities because the data was collected in the area of water protests in Isfahan: Tehran Karaj Shiraz Isfahan Ahvaz Abadan Sistan and Baluchistan Mashhad Rasht Tabriz Kerman Khuzestan Kermanshah Yazd 

After conducting a one-week pilot to measure the number of the tweets posted around these words and analyzing their relevance to the city, they collected tweets containing these words. Since in Persian there is no substitute for our immunities in words, it is easily possible to make mistakes in understanding words with the same spelling. One of these cases we encountered in this study is two similar words the “housing” with two readings of maskan and mosaken, and there is no means to identify the species in question, so this was removed. Or that the number of tweets containing the word count was lower than expected, which made our sampling incomplete, whereas the number of daily tweets extracted was between 500 and 2000 tweets per day in most cases, some words were used in a much smaller number of tweets and this meant that we did not have a uniform data to compare and draw conclusions, such as the word (mosque). The word in question covered more than the topic of the city where the content of the tweets was checked using the TF-IDF method (like quarantine), in which we found that tweets containing these words were more of an issue than a city issue. A number of words were removed and the remaining words were 22 words:

1. Tehran, 2. Karaj, 3. Shiraz, 4. Isfahan, 5. Ahvaz, 6. Abadan, 7. Sistan and Baluchistan, 8. Mashhad, 9. Rasht, 10. Tabriz, 11. Kerman, 12. Khuzestan, 13. Kermanshah, 14. Municipality, 15. City Council, 16. Street, 117. Bus18. Metro, 19. Shoppers, 20. Gasht Ershad, 21. Working Children, 22. Valieasr 

Then tweets containing these words were extracted in the desired interval. There are various methods for text analysis and text mining on Twitter. One of them is to draw a word cloud that represent the most important words in the text, and the frequency of a word is usually used to indicate it. This method is very suitable for summarizing text but does not specify the context of the text, meaning that if two words with the same frequency are in the image, it is not clear that they were used together or used separately in the text (ibid., 66). There are several ways to get keywords in a text. One method is TF-IDF. This method is based on weighting the words to get their meaning in the text. For example, if the word “is” is counted in a text, we will run into numerous “is”, so the TF (term frequency) value is high, but the word “is” is everywhere in the text and has a lot of generality, so the IDF (inverse document frequency) is low. So, a word has to have high TF-IDF so that we can count it as a keyword component (towardsdata science.com) Another method is YAKE which is fortunately applicable to different languages and is an automatic word extraction method that gets the most relevant keywords by statistical method and weighting to words without requiring a dictionary or external text be compares text to itself and is one of the best practices for languages other than English (campos et al., 257). There are suitable extensions for text mining in the Orange application, which can be used to output TF-IDF and YAKE by uploading the same tweets that were collected in 4 weeks took it. We will then have the keywords that have been obtained, whose conti-nuity and content we can see. 

RESULTS

‘Working Children: As we can see in Fig. 1 in this chapter, the extracted keywords related to working children are: support, cartons, payments, spending, corruption and poverty. The image of the working childrens words cloud shows that the most used words in tweets include the Tehran, support, poverty, country and pay (Fig. 1).

Working Children actually the most important topics that are constantly mentioned with working children are these words. As can be seen, the only word related to the city is Tehran. The mention of Tehran as an important word when it comes to working children shows that in the eyes of Twitter users the city is associated with the urban dilemma of the working children. In this way, Twitter users perception of child labor is a social problem mixed with poverty and corruption, on the other hand, the word support reflects the users perspective on this issue, in the sense that child labor is a problem that needs support, which is why accompanying the word Tehran with these three words, poverty and corruption and sup-port, shows us that there is probably a two triune relationship between poverty and corruption despite Children working in Tehran are in the minds of users who consider solving this problem in the interest of support (from whose side and from whom?)

1. Gasht Ershad: (a type of morality police in Iran which take care of Hijab not being fell from womens head in Iran) is a theme that covers various aspects, from womens issues to a kind of urban issue because it is one of the areas that plays a role in the urban landscape. 

2. Keywords Gasht Ershad and its Cloud Words (Image 2) More Showing The picture of this phenomena. The keyword ‘video shows this. Therefore, only the texts generated on Twitter alone are not enough to show users mental perspective on the subject and require analysis of the films, but nevertheless we can con-clude that, in addition to showing the films and the main issue of the veil in this topic, an urban issue is also formed in the minds of users because the words associated with the city are in the word cloud. It is a street and Tehran. Although this is an issue for the whole country, it shows that Twitter users have an understanding of the urban issue of Gasht Ershad only in Tehran. 

3. The word peddler, whether in cloud words or in keywords, has a great correlation with urban issues, metro, street and municipality, and the word “city” itself shows that this issue on Persian Twitter is an urban issue. (Fig. 3) 

In fact, Persian Twitter users have used this word along with urban manifestations and thoroughfares, suggesting that the perceived space in their minds is an urban image of the peddlers. On the other hand, this social issue in the minds of users is not limited to the city alone. The existence of the word kid also suggests that it has a lot of correlation with children in understanding citizens. But the point there is that despite the presence of the word municipality in these tweets, we dont include the word peddler in the key-words of the tweet contains ‘Municipality (picture 18)‎. It can be said that when it comes to peddlers, the municipality is the main influencer, but when it comes to the municipality, this relationship is not twofold and therefore in the minds of users, municipality is an influential factor in the issue of peddlers.

4. Abadan, Khuzestan and Ahvaz: You observe that the specific names of the cities had different results. Abadan, Khuzestan and Ahvaz are associated with the name Isfahan, and since data extraction was during the protests of Isfahan farmers, this is important. On the other hand, of course, the word city and protests are just keywords. The method of extracting keywords also includes Isfahan and Khorramshahr, which indi-cates the importance of a contextual issue at the time in question (Fig. 4, 5, and 6). The correlation of the words Isfahan and Khuzestan shows that the citizens present on Twitter consider these two cities similar in the issue of water shortage or even see the two as reason and effect in the sense that the issue when the context is the time in question is the street protests of farmers in Isfahan, citizens also speak from Khuzestan and consider this an integrated issue. Perhaps this can be explained by users environmental awareness of the water supply issue in these two provinces. 

5. Isfahan in Isfahan Keywords The notable issue is that the word ‘city is listed as a separate keyword. Given that the Isfahan protests have caused the production of tweets to increase on Thursdays and Fridays every week. In this word a higher number of tweets has been extracted than in other cities. So, looking at (Fig. 7) we find that the city has been the main topic of discussion in tweets about Isfahan. On the other hand, the hashtag #اصفهان is also visible separately in this word and can be the understood according to the temporal posi-tion of the topic. Because when a topic arises in an urban space as a protest or rally, it is common for hashtags to appear in conjunction with (crush or against) the same topic on Twitter for it to become a trend. 

One such issue has been the hashtag #metoo on Twitter in reaction to the harassment of women, which may name as a clear example of a topic becoming a hashtag of it. The presence of the word‎ farmers, water and protests in Isfahans word cloud confirms the high solidarity between urban space and Twitter space on this issue. For this reason, it can be said that Persian Twitter is sometimes also in the role of a media to inform everyday events, be-cause it reflects what is happening in the urban public area. The existence of the words industry, transmission, industrial and province in the word cloud shows that users are arguing over the reasons and results of the dilemma that has led to the farmers protests, in fact the Isfahan word cloud shows us an urban public sphere where users are engaged in generating social space. Just like when several citizens are discussing an issue in a city crossroad.

6. Tehran is the word with the highest number of the tweets, with its generated content reaching more than 2,000 tweets on some days. So, this number of tweets were higher due to the high volume of keyword data. But the remarkable thing is that the most important keyword on this list is ‘university. And obviously the word city is a particular word among Tehrans tweets, which has had a high frequency. So, we can conclude that when speaking of the city of Tehran, the word ‘university has been the most common word (Fig. 8).

Although Tehran as the capital has many urban trappings, Twitter users have spoken of universities in the city. As a key word, the university reflects the importance of its location in the city of Tehran. Of course, the synchronicity of Tehrans data extraction with a photo of graduates facing the head at the University of the Tehran is not so unrelated. (www. mizan.news/en/news/770923) Twitter events have the even been used in the news about this photo. For this reason, the university in Tehran as an urban place is an important issue that Twitter users have paid attention to, and as mentioned, it has been facing the university and not at the university, which makes it more of an urban landscape.

7. Karaj, Rasht, Tabriz and Mashhad the keywords in the content of the tweets of these four cities have been the word Tehran. On the other hand, in the content of tweets about Karaj, the numerical value of Tehran, (0.18) is higher than the numerical value in tweets about Tabriz and Rasht (Fig. 9-12). In the keywords of Mashhad and in the cloud of the words “Imam” you see‎. The description and understanding of Mash-had by Twitter users from Imam Reza Shrine, who turned it into the main branch of the city, is conceiv-able. In the sense that users speak of Mashhad, the overall picture they remember is this Shrine. In the Mashhads word cloud, we also see the presence of the word Qom, which shows the religious solidarity of these two cities in the eyes of users. 

Keywords and the word cloud of Rasht Karaj and Tehran are strongly correlated with each other from the perspective of Twitter users, as can be seen in keywords and cloud words in Karaj. Users of these two cities have a great relationship with each other. The presence of many verbs and the word ‘life also shows that in the tweets of Persian users there is a lot of continuity in daily life between Karaj and Tehran.

Rasht is also associated with the word Tehran in users opinion. This accompaniment perhaps bears the in-signia of the geographical closeness of these two cities. On the other hand, the presence of the words “municipality” and “clock” in the cloud of Rasht the words, which refers to Rashts municipal square, indicates that the users talk more about the city than urban landscapes. In Tabrizs word cloud, the remark-able thing is that the names of a large number of cities come from, Tehran, Isfahan, Zanjan, Mashhad and Shiraz. In fact, it can be said that when Twitter users speak Farsi from Tabriz, they do not refer to Tabriz independently and speak of it in contrast to other cities‎. 

8. Kerman, Shiraz the most important keyword extr-acted from content related to these cities is Isfahan. Given the range of the farmers objections, this is a plausible case. On the other hand, in the keywords related to Shiraz, Tehran is also used with a number correlation close to Isfahan (Fig. 13, and 14).

The accompaniment of two words Tehran and Isfahan in tweets related to Shiraz shows that within the time frame of the farmers protests in Isfahan, this issue has also appeared in tweets containing the word Shiraz, and on the other hand, the existence of the word Tehran also indicates that in users have also spoken about the capital of country. 

9. Sistan and Baluchestan: The order of keywords changes a lot compared to the rest of the cities. The presence of the word Khuzestan as a keyword in tweets related to Sistan and Baluchistan is notable. As can be seen in Fig. 15, the words Iran, Khuzestan and Khazar have been extracted for Sistan and Baluchi-stan (Fig. 15). The word Isfahan also appears in the word cloud. As visible in the word cloud in Sistan and Baluchistan, users from all cities far from the center have named in their tweets. Khuzestan and Kurdistan are the point that in the eyes of users far from the center of the issue are connected. The existence of the word Balouch, village and Afghanistan also shows that the greatest emphasis of users is on geographical issues in this region. 

10. Kermanshah: as well as Isfahan a separate hashtag has been extracted as a keyword. #کرمانشاه and the quake show tweets about this city are the generally different from the composition of tweets about other cities (Fig. 16). The overall theme changes of tweets related to Kermanshah shows that Persian Twitter users are talking about an issue that has affected the lives of citizens when they talk about the region more than they tweet in the form of landscapes or urban issues, which is the Sar-Paul Zahab earthquake. In the word cloud the word Sarpol is also the observed‎. 

The difference of these words from others requires the careful contemplation of the urban issues of this region in the view of users. But in the general infor-mation that we see, the existence of the Kermanshah hashtag indicates the need to pay attention to this issue, which users are trying to attract peoples atten-tion by generating it. 

11. In the words of the “city council” and “munici-pality”, the content of tweets has been associated with the keyword Tehran. In addition to the content related to the city council, we also see the keyword “muni-cipality” in addition to keywords (Fig. 17 and 18). In the word cloud of the City Council we see that the elections, appointments and members have come. The words suggest that users have been talking about Tehrans city council elections and the appointment of a mayor. The existence of word of the meeting and the problems also indicates that there has been talk about city council meetings in general. But probably all of these tweets were about the Tehran city council. 

In the cloud of municipal words also the word Tehran and Zakani indicate that it was spoken of the mayor of Tehran. On the other hand, the presence of the word city, district and waste in the word cloud is the fact that users have talked about urban issues related to the municipality.

12. Street: The key content of street tweets is also, like the previous, the word Tehran. On the other hand, the word city and alley, which represents the urban content of these tweets, is also just keywords. On the other hand, in the cloud of the words of the street, the 

words ‘people, ‘protest and ‘revolution are visible. Presumably the word Revolution is related to Revo-lution Street (Fig. 19). We see the word pedestrian and car, which shows that tweets containing the word street are related to the daily urban life of users. 13. Bus and Subway: keywords related to bus and subway also see the word Tehran, like the previous content. Subway And bus have appeared reciprocally in each others keywords. On the other hand, in both cloud words “pedestrian” is visible (Fig. 20 and 21). 

In the cloud of the words bus and subway, what is noticeable is morning, station, ride, traffic and street, indicating that users have tweeted about their every-day urban issues. On the other hand, the presence of the word Tehran in keywords indicates that users generally write about Tehrans buses and subways, and that there is less talk from other cities. In this way, both about the street and about the bus and subway, we see articles about everyday urban life in Tehran. 

14. In the report of words related to Valiasr (a famous street in Tehran) tweets, we see words related to urban public space. Street, square, crossroad, revolution are all words that indicate conversations about the public space of Valiasr in Tehran. Because the word Tehran also appears separately in the keywords. In the word cloud related to Valiasr, we can also see the words Tajrish, Lemiz, Pedestrian, City and walk, indicating that users have been tweeting about their daily lives in the vicinity of Valiasr in Tehran. The authors delibe-rate choice about a specific place in the capital was due to the representation of specific urban places online. On the other hand, the overall picture that the word cloud gives us is a favorable urban public space for users. In the word cloud, we do not see signs of specific verbs or nouns that indicate that users have spoken negative things about Veliasr.

DISCUSSION

In this study, using text mining and keyword extr-action, we found that most of the keywords used in tweets related to the city of “Tehran” and “Isfahan” had the highest frequency, so in Twitter conversations the most we observe is the reproduction of the import-ance of the central Iranian cities. Along media and propaganda, Twitter is where the dominance of power and governing discourse is reproduced. Thats why various informal voices are not heard in it. In fact, communication between citizens implies a dialogue between them that carries a message about the city and local issues, the absence of local issues and the boldness of the central theme “Tehran” on Twitter shows that most Twitter users speak Farsi from the center, and this centralism is the same topic pursued by other media as well. As Castells emphasizes in the discussion of the space of flows, local culture breaks down in the network of relations in the space of streams, imposing its own non-historical and net-worked logic on the places. (Castells, 495) In the extr-action of the keywords, we also observed that the recurring keyword in all tweets was “Tehran” and “Isfahan”, as a result, local communities do not play a role in tweeting, and the text content of the platform is under a centralized identity. On the other hand, due to the high speed of Twitter (short text and synchro-nization with daily life), many people enter and exit in a short period of time and are not subject to local law. In light of this research, it can be confirmed that even during the conversation about the city on Twitter where location is a constant component of the text, the timelessness and spatiality of Twitter and the negation of local culture have turned it into a space of flows. In fact, with a structuralist look if we look at Persian Twitter in the urban sphere, we see a space of layers of information about the city where users tweet about the city and others see these tweets, like and retweet. In this way relationships are formed around these topics as well. The relationship between this online space and the urban space form a space of flows‎ where people connect to the vast network of infor-mation and temporarily and at high speed form the relationships. We can answer by using the research findings and emphasizing Lefebvres spatial perspec-tive that different urban themes in Persian Twitter are, firstly, centralist and based on the central cities of Iran, and secondly it covers the everyday happenings of urban space such as the protests of Isfahani farmers in most of its themes. It represents two characteristics, namely centralism and attention to bold urban phenol-mena, the city and cityscape in Persian Twitter, and for this reason refining this space is suitable for understanding cultural integration in cities. Lefebvre speaks of a dialectic that encompasses the perceived, imagined, and experienced space. The different dimensions of social life are a set of subjective under-standings of the physical and social world, imagining this world in different cognitive ways is in our thinking, and living in a world of social relations in which humans behave based on values, make collec-tive decisions, produce different laws and morals and meanings. Perceiving and imagining are mental and informational activities, a social category that makes up part of the experience of social life. Perception is the subjective reception of the world experienced, and imagining is a particular type of perception that produces creative information about the experienced world. And the experience of life means perceiving, imagining and producing society. In the process, sepa-rating these three categories from each other is not an easy task. The result of these three is called Spatial Practice, a mediator between impression and experi-ence that blends and separates spatial representation and representational space simultaneously from each other (Fush, 2018, 16-17). Similarly, it seems that there is also a space precept about the city in Persian Twitter. In the keyword extraction section, we showed that what is evident in most tweets about the city is the production of words based on the center. Persian Twitter users perception of the city is a centrist view, as they agree on one word and that is “Tehran.” This centralist gaze results from their perception of the experience of living in this world. If we return to the original question of this research: How is the city and cityscape represented in Persian Twitter? We can answer using the findings of the research and empha-sizing the spatial perspective of Lefebvre that differ-rent urban themes in Persian Twitter with regard to extracting keywords from it are, firstly, centralized and based on the central cities of Iran, and secondly, everyday events of urban space such as protests It also covers the farmers of Isfahani in most of its themes. It represents two characteristics, namely centralism and attention to bold urban phenomena, the city and city-scape in Persian Twitter, and for this reason refining this space is suitable for understanding cultural integration in cities. 

CONCLUSION

Discussing and concluding in the word cloud and reporting keywords related to extracted tweets, we found that there were a few hints in the various words we searched about the city –

1) Tweets of words related to urban problems, urban transport, urban public institutions, urban landscape were all shared in one keyword: Tehran:

2) Cities with keyword Tehran were introduced; 1. Cities that were tagged with the keyword Isfahan 2. Cities with Tehran keyword.

3) Tweets about the names of different cities are divided into two categories It could be: 3) Tweets about the names of cities in the word cloud section indicate that the word water, which was the main issue when tweets were mined in urban protests, was also used a lot. 

4) The word city was used among all the common keywords. This indicates that most of the tweets checked were related to the city. 

5) The presence of the word protest or protests in the cloud indicates the thematic relevance of the tweets with the time of their extraction, coinciding with the protests of the farmers in Isfahani. 

6) The only public place in the city that is present in almost all tweets is the word street or street.

7) The university keyword in the tweets Related to Tehran is a sign of the importance of this urban place. On the other hand, at the time of extraction of the tweets, a photo of graduates in front of the head was taken at the University of Tehran, which was the subject of many positive and negative conversations. However, on the other hand, the word university appeared separately and with a high number among the Yake report keywords of tweets related to Tehran, indicating its importance independent of the relevant photo topic. 

8) Tweets about convenience store and street are most relevant to urban issues. Because the most city-related words are seen in their keywords. 

9) Tweets about Gasht Ershad and Working Children have the least relevance to urban issues because the most important and primary keywords are hijab, film and cover, and the only related words are street and Tehran of low importance. 

10) The word ‘Photo appears in almost all keywords. The word suggests that many tweets are accompanied by photos. The description and explanation of the city is done by displaying photos and videos by users. 

11) Looking to the word cloud of cities, the most important image of a city is shown to us and roughly corresponds to what is already in the readers mind:

According to the table above extracted from the word cloud it seems that what exists in our minds as matters of a city or a general image of a city it matches whats being written about cities on Twitter.

ACKNOWLEDGEMENT

We, Maryam Peimani, and Abdolhossein Kalanatari, hereby acknowledge that I have read and understood the conflict of interest policy of Asian Journal of Social Sciences and Legal Studies. I certify that I have no financial or personal interest in any matter that may be affected by my work. 

CONFLICTS OF INTEREST

I agree to disclose any potential conflicts of interest that may arise in the future. I understand that failure to disclose such conflicts.

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Article Info:

Academic Editor

Dr. Sonjoy Bishwas, Executive, Universe Publishing Group (UniversePG), California, USA.

Received

December 10, 2023

Accepted

January 14, 2024

Published

January 21, 2024

Article DOI: 10.34104/ajssls.024.01018

Corresponding author

Maryam Peimani *

Social Science Research, Faculty of Social Sciences, University of Tehran, Tehran, Iran.

Cite this article

Peimani M., and Kalantari A. (2024). Urban themes constructed in the Persian twitter, Asian J. Soc. Sci. Leg. Stud., 6(1), 1-18. https://doi.org/10.34104/ajssls.024.01018 

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