2c:null 2d:["$","title",null,{"children":"SM3809 Software Art Studio Final Project - The Journeys | stevenzhou01 | Observable"}] 32:T417, | Risk Areas | Solutions | | ----------- | ----------- | | The content of text data. The data may not be suitable for the generation. | Analyze the text content -- What's the topic of the texts? Is it a question or a statement? What's the sentiment of the text? What is the first word, and the last word? | | The format of articles. Not specifying the article format may make the artwork unclear.| Confirm the formats of generative articles. It's better to focus on one format first, such as the interview format. | | The algorithm of generating the process. There may be some limitations of the algorithms. | Thinking about the generating process. What's the input? What will be the output? What's the limitation of the algorithm? | | What to show in the final work? | The final work should be the generative articles. It's better to focus on one long article instead of several. Make the webpage to show the prototype| | The meaning of nonsense generative articles. | Think about what the artwork intends to convey to the audience?33:T4a4,This prototype shows the generated conversation between two "persons". The topic is the European refugee crisis. I respectively collected 10 quotes for each stance, i.e. pros and cons of the refugee crisis. Then I used N-grams to generate the "sayings". By clicking the button "speak", it gradually generates the sentences. The person in each generation will exchange to make the effect of "conversation". Limitations: In this prototype, the text data is not enough, so there will be some repetitions at some points. For the final, I need to collect more text data. Also, some sentences are nonsense. How to deal with those nonsense text is also an important question. Moreover, as I mentioned in the generating process, there should be a connection between the current generated sentence and the previous generated sentence, but the current prototype hasn't included consistency. This is to achieve the effect that the two "persons" can make a conversation. However, the "last word" won't work well to achieve this effect in the original generating process. Last, but not least, the prototype only shows one topic, while the final work can have more topics as mentioned in the proposal.34:T66a,1. How to assess the quality of generated texts? Why can we say a generative text work is good?
Conduct research about human and computer-generated texts and make a comparison. It's easy to make computer-generated text nonsense. For this project, I don't plan to make the texts nonsense, so it's important to make it clear what the texts are going to convey to the audience. 2. What is the message that I want to convey to the audience through the generative texts?
Based on the previous idea, there would be two possible options. One is the two "persons" with different ideas keep arguing and not listening to each other until the end. Another one is the two "persons" argue but come to an agreement in the end. I prefer the latter idea which can be a better effect on this artwork. 3. Why computer-generated?
This is a technique-related problem about what the argument is between the technique and the artistic concept. For this project, the topic of discussion should be more specific. "Refugee crisis" is a very broad topic. A more specific topic is better to make the argument more meaningful and reasonable. For example, if the topic is "refugee crisis", the cons to it are mostly from the members of some far-right parties, which is not worth discussing.
Based on the second idea mentioned in 2, I can collect different arguments with different stances. Then using the computer algorithms, it can analyze and find the common points between different opinions, then reach an agreement. The message of the artwork is to find a way that how different opinions can exist and communicate with each other.35:Teb2,1. Why uses computer-generated text instead of human writing?
This question is raised by myself when thinking about the difference between computer-generated text and human text. I think there is no solid reason for this project to use computer-generated text since if the project intends to analyze the different opinions and finds the agreement, the text written by a human would make more sense than the computer-generated text. There needs research about what are the advantages that computer-generated texts have compared to human writing and what the computer-generated texts can create. Then rethink what this project wants to convey to the audience. 2. What is good and interesting art?
"Good art does things that we didn't predict in some way. It has an ability to surprise or teach us something new or maybe view something in a different light." - *Code Goes In, Art Comes Out, Tyler Hobbes*
From what Hobbes mentioned, programming has the attribute of being predictable due to the rules and system. Therefore, how to create something unpredictable and surprising in the generative text work is what I need to think about. If the work just shows the nonsense text or the two "persons" keep generating texts in their opinions, the work won't show the "surprising thing" to raise the thinking from the artistic perspective. 3. Computer-generated texts
Reference: [Reading Computer-Generated Texts, Leah Henrickson, 2021](https://www-cambridge-org.ezproxy.cityu.edu.hk/core/elements/reading-computergenerated-texts/402FD0C808FE7E92E8BC9E88EBE23CA6)
Natural language generation (NLG) is the process wherein computers produce output in readable human languages. Computer scientists have long been studying to generate texts by computer programming that are **indistinguishable from human-written text**. Also, today we may read the texts as a part of our daily routines without being aware of their being computer-generated. There are two approaches to NLG in general: rule- or template-based approaches and machine learning approaches. 4. Related works:
I. [Urbs Media](https://urbsmedia.com/)
Urbs media uses open generate news and stories. Many articles are short and generated from a bunch of existing relevant articles. Also, the articles tend to show the study, findings, or facts mentioned from other sources. There are few analyses or statements with opinions. II. [Endless Ads: a solution for every problem, and a problem for every solution, Vera Chellgren, NaNoGenMo 2021](https://github.com/NaNoGenMo/2021/issues/47)
The project from the 2021's NaNoGenMo continuously generates advertisements with the format of Q&A. The format of the advertisements follows raising the problems and giving the solutions, which is regarded as a typical advertising method. Also, the solution provided by the last ad becomes the problem for the next ad. 5. Further developed idea.
Based on the idea mentioned previously, the problem is the message that the project intended to convey to the audience is unclear. Even though I hope to use a computer algorithm to reach a virtual conversation between people who have different opinions, the effect and value of the project wouldn't be as good as having a real conversation with those people. The different opinions could not really communicate with each other just by computer algorithms. Also, since the previous idea needs the conversation to have a logical discussion, it is hard for the computer algorithm to achieve and totally avoid the nonsense sentences. Therefore I change the direction of the project to generative fiction. Since fiction describes imaginary people and events, it can allow nonsense sentences to some degree. 36:Tda7,[***YeetWords***](https://github.com/verachell/YeetWords) is a domain-specific language for text substitution implemented in Ruby and designed by Vera Chellgren. Here is the [tutorial](https://github.com/verachell/YeetWords/wiki/QuickStart-Tutorial) of YeetWords. This language generator can generate sentences by substituting the words from the word list, which is also the algorithm to make the work *Endless Ads: a solution for every problem, and a problem for every solution*. The most important thing is the ideology of how to use text substitution to generate sentences. It's also not only about generating a single sentence, but it also teaches how to make the sentences coherent to make the text more readable. The main idea of this generator is to create sentence templates and keywords to create the consistency and narrative of stories. For example, in the work *Endless Ads*, the algorithm firstly generates a noun phrase, then puts the noun phrase into the question template. Then it generates another noun phrase to put in the solution template as the solution. The second noun phrase will be put into the next question template to form the question as well as create consistency.
**Example generated by YeetWords:**
*"A man approached carrying a beach towel. Then he said "My name is Nigel and I have a mouse". A woman approached carrying a USB drive. Then she said "My name is Mylee and I have a fish". A woman approached carrying a pencil. Then she said "My name is Ann and I have a fish". A woman approached carrying a diary. Then she said "My name is Roselyn and I have a spider". Suddenly, lightning struck Roselyn and she died instantly. Ann, Nigel, and Mylee looked on in horror, stunned. "* **Code:**


ASSIGNLIST pet = "dog" + "cat" + "rabbit" + "weasel" + "rat" + "spider" + "snake" + "mouse" + "fish" + "lizard"

ASSIGNLIST smallitem = "fountain pen" + "diary" + "USB drive" + "lanyard" + "travel mug" + "skateboard" + "stapler" + "pencil" + "apple" + "beach towel"

 
GEN person 4 binary

pet 1 pet

carrying 1 smallitem

GENEND


ASSIGNLIST mysentence = "A _PERSON.MANWOMAN_ approached carrying a _PERSON.CARRYING_. Then _PERSON.HESHE_ said "My name is _PERSON.NAME_ and I have a _PERSON.PET_""


ASSIGNCATALOG mywords = pet + smallitem


LOOP 4

WRITE mysentence mywords

SHIFT PERSON 

LOOPEND


NEWLINE

SHUFFLE person

RECITE allppl = person.name + all

WRITE allppl mywords


NEWLINE

ASSIGNGEN calamity = person 1

ASSIGNLIST whathappened = "Suddenly, lightning struck _CALAMITY.NAME_ and _CALAMITY.HESHE_ died instantly"

ASSIGNGEN person = person - calamity

SHUFFLE person

RECITE allppl = person.name + all

ASSIGNCATALOG mywords = mywords + allppl

ASSIGNLIST afterwards = "_allppl_ looked on in horror, stunned"

WRITE whathappened mywords

WRITE afterwards mywords

ASSIGNLIST defines the array of words
GEN is like an object in JS.
ASSIGNCATALOG defines the range of word arrays
WRITE: Generate the sentence.
LOOP: Loop the GEN.
SHUFFLE: Randomize the person's order to make the text more natural.

 ASSIGNGEN calamity = person 1 

ASSIGNGEN person = person - calamity

: Pick one person to "let it die" from the person's list. This code uses persons' names to create consistency.
37:T4f8,**1. Final Confirmed Project Idea**
Based on the analysis of *YeetWords*, the project creates a generative fiction. First, since the refugee crisis is a severe global issue, the project decides to stay on this topic to show the solicitude about social issues. The topic will be more specific on the journeys of refugees migrating to Europe. An interview I worked with is talking about the European refugee crisis, which is titled [*Nathalie Suthor on her sea rescue journey through the Mediterranean.*](https://www.paradigmhaus.com/post/nathalie-suthor-refugee-sea-rescue-mediterranean) by the *Paradigm Haus Magazine*. It mentioned many similar stories that happened to refugees when they "escaped" from their countries. Human trafficking, starving, rape, refugee camp, shipwrecking, and police brutality... all these have become a "fixed" procedure that is highly possible to be experienced by the refugees during the migration. Therefore, the generative fiction about the journey of refugees intends to indicate that those disasters may happen repeatedly and constantly to a large number of refugees. The randomness of the algorithm also means the uncertainty of the journey. The fiction is to arouse people's thinking and awareness of the experience of refugees. 38:T526,**2. Generating Algorithm**
The generating algorithm is inspired and adapted by YeetWords designed by Vera Chellgren. YeetWords is a domain-specific language for text substitution implemented in Ruby. The algorithm of this project is implemented in p5.js. The whole fiction consists of 29 individual stories ordered by the years from 2004 to 2032. The technical concept "GEN" and "ASSIGNLIST" are adapted in the project. "GEN" is for storing a character's information. In this project, "GEN" is implemented by JSON to install the generated characters' information including names, genders, origins, and identities. Since p5.js does not have the function to check a name's gender automatically, the algorithm includes the process to check the genders of the generated names. Moreover, "ASSIGNLIST" is for the text substitution to generate coherent sentences, which is implemented by JSON and arrays. The stories have 7 types of story development. Each type of ending depends on the possibility generated by arbitrary numbers. The map below shows the generating process of the fiction's story development. Moreover, based on the generated fiction, the algorithm also generates a map that shows the immigrating routes of migrants.

39:T629,**3. Text data**
- The origins of migrants are based on the research and statistics of migrants in Europe, such as Iraq, Syria, etc.
Sources:
[The Top Origin Countries For Refugees In 2018, Forbes](https://www.forbes.com/sites/niallmccarthy/2019/06/19/the-top-origin-countries-for-refugees-in-2018-infographic/?sh=4ac95ee16cce)
[Mid-year Trends 2021, UNHCR](https://reliefweb.int/sites/reliefweb.int/files/resources/618ae4694_0.pdf)
[Migration to Europe in charts, BBC, 2018](https://www.bbc.com/news/world-europe-44660699) - Characters' names are the 150 male and female common names in 2022. The names are not from those origin countries to avoid the racism issues.
Source: [Most popular baby names of 2022, Babycenter](https://www.babycenter.com/baby-names/most-popular/top-baby-names-2022?startIndex=100) - In each story, there is a timeline to indicate the important events about the refugee issue each year that happened in reality. After 2022, the timelines are fictional. Source: [Timeline of the European migrant crisis, Wikipedia](https://en.wikipedia.org/wiki/Timeline_of_the_European_migrant_crisis) - The story also mentioned the reasons that can cause refugees. Source: [Biggest Causes Of A Refugee Crisis, Epimonia, 2019](https://epimonia.com/blogs/news/biggest-causes-of-a-refugee-crisis) - The storyline is adapted based on the interview by Paradigm Haus magazine, [*Nathalie Suthor on her sea rescue journey through the Mediterranean.*](https://www.paradigmhaus.com/post/nathalie-suthor-refugee-sea-rescue-mediterranean)3a:T507,**4. Artistic Concept** The project creates a generative fiction describing the imaginary stories that happened to the migrants based on the realistic situation. The destination of migrants in the story focuses on Europe because of the European migrant crisis in 2015. The project focuses on the situation and problems experienced by the migrants during the migrating process and intends to arouse people's awareness of the refugee issue. The whole fiction consists of 29 stories ordered by the year from 2004 to 2032. The second paragraph of each story is one of the most important events that happened in that year on the refugee issue, which made a timeline of the whole fiction. The story development depends on the possibility generated by arbitrary numbers, which indicates the uncertainty of the migrating journeys. The fixed and repeated format of the stories indicates the shared experiences among a large number of migrants. They have a high chance to meet similar troubles during the journeys. The fiction also includes the stories that may happen in 10 years after 2022 as the future prediction of the refugee issue. In the end, it calculates the total survivors and victims in these 29 years. 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The idea is to make an online generative magazine. The magazine is constructed with computer-generated texts on topics of social issues. The magazine doesn't write original articles by itself, instead, it chooses articles with specific topics from other magazines as text data to form its narrative. The topics will focus on social and political topics, such as gender, race, refugees, economic issues, and more. Meltwater is a good platform to search for press releases and articles.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":62,"value":"## Apr 7th, 2022 -- Proposal","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":66,"value":"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":34,"value":"## Apr 15th, 2022 -- Risk areas","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":46,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":92,"value":"## Apr 21th, 2022 -- Research and Revised Idea","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":100,"value":"* #### N-grams and Markov Chains\nIn the fields of computational linguistics and probability, an n-gram is a contiguous sequence of n items from a given sample of text. An n-gram model is a type of probabilistic language model for predicting the next item in such a sequence in the form of an (n − 1)–order Markov model.
\nA Markov chain is a mathematical system that experiences transitions from one state to another according to certain probabilistic rules. This technique has been used by many generative artworks, so it doesn't seem to be innovative if it's just directly applied. This generation method should be evaluated if it fits into the scenario of the artwork.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":115,"value":"* #### Revised Idea\nIn the revised idea, the magazine will focus on showing different opinions on one social issue. As inspired by A New Old Conversation, Yang Ren, the generated magazine will be a \"platform\" to make the different opinions have a virtual conversation. Therefore, the text data will be the quotes from people who hold different opinions, generally divided into \"pros\" and \"cons\". By using N-grams to make these two different opinion holders exchange and communicate their opinions. The output will be a generated article showing the generated \"conversation\".","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":123,"value":"* #### Generating Process","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":127,"value":"

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":139,"value":"## Apr 22th, 2022 -- First Prototype","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":143,"value":"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":149,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":160,"value":"## Apr 23th, 2022 -- Further Developed Idea","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":165,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":175,"value":"## Apr 27th - 28th, 2022 -- Research","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":191,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":232,"value":"## Apr 28th - 29th, 2022 -- YeetWords, A Language Generator by Vera Chellgren","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":240,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":363,"value":"**Analysis: **
\n*YeetWords* mainly uses text substitution to generate context-free languages. As the author mentioned, the algorithm is better for generative fiction. Fiction can sometimes allow some nonsense text generated by the algorithm. The good function is that *YeetWords* can deal with the sentence formats including punctuation marks, capitalizing the first word, and more. The articles generated by the YeetWords algorithm will be in a repeated format following some same rules, but it also has some limitations. For one repetition of generative text, the text length may need to be short for the quality of the text. Also, the repetition creates inflexible article formats to make the writing style and article content unattractive to readers. Moreover, the artist should consider the meaning of repeated text from an artistic perspective.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":275,"value":"## Apr 30th - May 1st, 2022 -- Implementation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":283,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":296,"value":"**2. P5.js Implementation**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":301,"value":"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":313,"value":"This is the implementation of YeetWords in p5.js, which creates a similar paragraph to the output example of YeetWords. The ASSIGNLIST is coded by \"array\", and GEN is coded by JSON. Since p5.js doesn't have the function to detect the gender pronouns of people's names, the code involves a part to check the gender pronouns. The \"dead person\" uses the function \"splice\" to remove from the person's array. Also, the shuffle function is defined separately. Moreover, the sentence format also needs to be defined separately such as quotation marks, periods, capitalized letters, etc.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":318,"value":"## May 2nd - 7th, 2022 -- Generative Fiction, *The Journeys*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":328,"value":"**1. Overview**
\n *The Journeys* is a generative fiction around 8700 words describing the imaginative journey of migrants from different countries to Europe for seeking a better life. The fiction consists of 29 individual stories from the year 2004 to 2032 written from the perspective of migrants. Each story describes the characters and their experience and troubles in the migrating journey. The stories are adapted based on the European migrant crisis in reality. Due to various reasons, a large number of people left their countries to seek a new life, but the journey is full of danger and uncertainty including getting lost, human trafficking, refugee camps, shipwrecking, and more. Most of the migrants share similar experiences and problems. Via the generative fiction, the project tends to arouse people's awareness of the situation and problems of refugees.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":381,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":334,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":401,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":410,"value":"**5. Screenshots of the work**\n\nWebsite\n

\n\n[P5.JS](https://editor.p5js.org/steven_zhou2000/sketches/fFaaHb_PI)\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":432,"value":"**7. Work**\n- [Website](https://stevenzhou01.github.io/TheJourneys_GenerativeFiction/index.html)\n- [GitHub](https://github.com/StevenZhou01/TheJourneys_GenerativeFiction)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W3b","stdlib":"1"}} 31:"$30:body"