2b:null 2c:["$","title",null,{"children":"In 2021, The word \"First\" appeared the most when women made headlines🥳 | Akhila Sulgante | Observable"}] 31:T40e,## Introduction Research has suggested that Women are significantly less likely to make headlines compared to Men. So, if women are underrepresented in the news, to begin with, what does it look like when women do make headlines? In order to investigate these inquiries, I have utilized data visualization techniques to examine the language utilized in headlines that center around women. The data utilized here is from [The pudding study](https://www.kaggle.com/datasets/thedevastator/women-in-headlines-bias?select=country_time_freqrank.csv) This notebook offers users the opportunity to discover the following information: 1. The frequency of word occurrence in headlines 2. The ability to filter and compare countries, and determine which country has the highest number of headlines relating to crime and other topics 3. Word frequency analysis by filtering themes This notebook takes a top-down approach, where users are first presented with an overview of the data, followed by a breakdown of the data by each individual factor.32:T49c,### Data Abstraction(What) In this section, I have described the attributes used in the above visualizations **Dataset Availability:** Static **Data Type:** Table **Dataset Type:** Categorical and Quantitative
_Note: Did not include temporal as I filtered the data to use just one year_ **Attributes** **`Country`** (_Categorical_): This attribute indicates all the countries that were considered in the analysis **`Theme`** (_Categorical_): These attribute indicate categories of topics **`words`** (_Categorical_): This attribute indicates the language used in the headline **`Country`** (_Categorical_): This attribute indicates all the countries that were considered in the analysis **`Frequency`** (_Quantitative, Sequential_): This attribute indicates the frequency of a word **` Frequency rank`** (_Quantitative, Sequential_): This attribute indicates the ranking of a word considering its frequency and word length **`Freq prop headline`** (_Quantitative, Sequential_): This attribute is _derived_ by dividing the frequency by count **`Count`** (_Quantitative, Sequential_): This attribute is _derived_ by summing the record counts by grouping countries 33:T64a,### Task Abstraction(why) In this section, I have mentioned the tasks that need to be performed to derive the above-mentioned made-up insights. Tasks are broken down into Actions and Targets Abstracting at the Task level 1. **T1:**
(_Insight_): Headlines featuring women tend to focus more on appearance and personality traits. Women are more likely to be described as "beautiful," "sexy," or "feisty"
Identify the most used word in the headline
Sub-task 1: (_Lookup_) the theme "Women Stereotype"(_Distribution_)
Sub-task 2: (_Identify_) the word that is used the most in the above-mentioned theme(_Extremes_)
Sub-task 3: (_Compare_) the word that is used to the made-up insight 2. **T2:**
(_Insight_): Headlines concerning women tend to feature crime and violence more frequently than empowerment-related topics. Compare stereotype and empowerment themes to know which is more used in the headline
Sub-task 1: (_Identify_) Identify the theme Empowerment(_Distribution_)
Sub-task 2: (_Identify_) Identify the theme Crime and violence (_Distribution_)
Sub-task 3: (_Compare_) Compare two themes to identify which more often appeared in the headline 3. **T3:**
(_Insight_): UK and US will have more headlines of women related to Empowerment or Seterotype Compare the valuation of most teams
Sub-task 1: (_Identify_) Identify the theme "Empowerment"(_Distribution_)
Sub-task 2: (_Identify_) Identify the theme "Crime and violence" (_Distribution_)
Sub-task 3: (_Compare_) Compare two themes to identify which more often appeared in the headline34:T6e9,### How In this section, I have jotted down different marks and channels used to enhance the Expressiveness and Effectiveness of the visualization. **Idioms:** 1. **` Scatter plot:`** This idiom includes 5 attributes and displays word distribution across themes, country, freq/count, and frequent ranking of words 2. **` Bar Chart:`** This idiom includes theme distribution over the sum of the count 3. **` Scatter plot:`** This idiom is interactive of the above one, where clicking on a particular theme will detailed scatter plot of word distribution over freq prop and frequency **Data Exploration charts** 1. **` Scatter plot:`** This idiom shows word frequency 2. **` Pie chart:`** This idiom indicates a composite of the theme of records 3. **` Bar chart:`** This idiom shows records in the dataset per country **Marks** 1. **` Points:`** To indicate words 2. **` Lines:`** To indicate theme **Channels** 1. **` Position on Common Scale:`** (_Distribution of words, theme count, frequency of words, records per country_): Position from the common scale is used to indicate the distribution of words, and theme count in the dataset. Arrangement: Express 2. **`Length`** (_Theme count, country records_): Bar charts are used to indicate the records per country and theme Arrangement: Express 3. **` Color Hue`** (_Theme_): Color hue is used to differentiate themes for words across the notebook Arrangement: Sperate 4. **` Shapes`** (_Country_): Shapes are used to indicate different countries across the notebook Arrangement: Sperate 5. **` Area`** (_Theme distribution_): The area channel is used to indicate the records of each theme in the data. It is used to express. 6. **` size`** (_Word count_): Size channel is used to express word count35:T6c4,tableauhtml = html`

`36:T722,tableauhtml2 = html `

`37:[{"id":"80e901c21441f322dac31c1cae480fa02765600576dad4821279b7b7f4ae6dd1e2b5c83e6c8639f8f5b54292c4f5d3fd98e0598ba58695b09b979deae2217862","href":"https://static.observableusercontent.com/files/80e901c21441f322dac31c1cae480fa02765600576dad4821279b7b7f4ae6dd1e2b5c83e6c8639f8f5b54292c4f5d3fd98e0598ba58695b09b979deae2217862","name":"Womenheadline.csv","type":"text/csv","lastModified":1676964808777.793,"size":637655}] 2f:{"id":"4a80c49bc0d239b4","slug":null,"title":"In 2021, The word \"First\" appeared the most when women made headlines🥳","thumbnail":"a9dfce4c406fcf3dd5d3b672c2498d3ac0ad0308dcfb29984c5464d7f1ba7108","access_level":"public","create_time":"$D2023-02-21T05:30:10.941Z","update_time":"$D2023-03-14T23:09:37.687Z","view_time":null,"first_public_time":"$D2023-02-21T05:30:10.941Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"e76678b312b62ab3","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":"starter_2024"},"version":414,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"667daa7e86be0e8e","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":null}],"members":[{"type":"individual","id":"667daa7e86be0e8e","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"In 2021, The word \"First\" appeared the most when women made headlines🥳","cells":[{"id":68,"value":"# In 2021, The word \"First\" appeared the most when women made headlines🥳","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":275,"value":"This notebook lets users explore the language used when Women Makes Headline across 4 countries(India, US, UK, South Africa)\n\nThis Visualization is created by [Akhila Sulgante](https://akhilasulgante.github.io/portfolio) as a part of [CS 7295 class](https://johnguerra.co/classes/data_visualization_spring_2023/) at Northeastern under the guidance of [prof. John Alexis Guerra Gómez](https://johnguerra.co/)\n\n[Walkthrough video](https://youtu.be/h93IW_QFLTI)
\n[Tweet](https://twitter.com/akhila_sulgante/status/1628470973237440512?t=1EuIRbOgdfbQxrDDOpxcBQ&s=19)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":281,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":328,"value":"## Compare countries by theme and word frequency using dropdown filters","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":197,"value":"htmlElement = html`

${tableauhtml}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":370,"value":"### Insight:\nThe word \"First\" appeared the most in all the countries, as opposed female stereotypical word","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":332,"value":"## Dive deep into themes and know the words used in each country","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":234,"value":"htmlElement2 = html`

${tableauhtml2}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":376,"value":"### Insight:\nSurprisingly the words that made headline in Stereotype category are not \"Sexy\", \"dive\" but words like \"Daughter\" and \"Mom\".","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":36,"value":"## Data Exploration","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":90,"value":"#### Data view with Navio","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":94,"value":"navio(CleanedData)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":170,"value":"#### Exploring words in the dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":175,"value":"vl.markPoint({tooltip:true})\n .encode(vl.y().fieldN(\"word\").sort(\"-x\"),\n vl.x().fieldQ(\"frequency\"),\n vl.color().fieldN(\"theme\"),\n vl.shape().fieldN(\"country\"))\n .width(800)\n.data(CleanedData)\n.render()\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"##### Exploring theme, country and frequency","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":76,"value":"#### Dataset composition by theme","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":80,"value":"vl.markArc({tooltip : true})\n .encode(\n vl.theta().count(),\n vl.color().fieldN(\"theme\")\n )\n .data(CleanedData)\n .render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"#### Exploring data to know count of the records by country","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"vl.markBar({tooltip : true})\n .encode(\n vl.y().fieldN(\"country\").sort(\"-x\"),\n vl.x().sum(\"count\")\n )\n .data(CleanedData)\n .height(200)\n .width(500)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":334,"value":"## Visualization problem abstraction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":336,"value":"### Target Audience\n1. Individuals who are interested in gender issues, media representation, and data visualization\n2. People who are interested in studying the language and portrayal of women in media and journalism\n3. Researchers, journalists, activists, and educators who are interested in exploring the relationship between gender and media representation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":338,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":348,"value":"### Madeup insights\n1. Headlines featuring women tend to focus more on appearance and personality traits. Women are more likely to be described as \"beautiful,\" \"sexy,\" or \"feisty\" [Selected]\n2. Headlines concerning women tend to feature crime and violence more frequently than empowerment-related topics.[Selected]\n3. UK and US will have more headlines of women related to Empowerment or Seterotype[Selected]\n4. India will have more headlines concerning Women's Seterotype and Crime","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":346,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":354,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":0,"value":"## Data preprocessing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"data = FileAttachment(\"Womenheadline.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"Considering only 2021 to eliminate temporal attribut","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"Data = data.filter(d=>d['year']=== 2021)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"CleanedData= JSON.parse(JSON.stringify(Data)).map(d=>{\n delete d[\"Unnamed: 0\"];\n delete d[\"Unnamed: 0.1\"];\n return d}).filter(d=>d['country'] !== \"all countries\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"import {navio} from \"@john-guerra/navio\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":191,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":200,"value":"divElement = {\n return htmlElement.querySelector(\"#viz1677047937546\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":250,"value":"divElement2 = {\n return htmlElement2.querySelector(\"#viz1677051725259\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":254,"value":"{\n let vizElement = divElement2?.getElementsByTagName(\"object\")[0];\n vizElement.style.width = \"1200px\";\n vizElement.style.height = \"827px\";\n let scriptElement = document.createElement(\"script\");\n scriptElement.src = \"https://public.tableau.com/javascripts/api/viz_v1.js\";\n vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":301,"value":"divElement?.getElementsByTagName(\"object\")[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"{\n let vizElement = divElement?.getElementsByTagName(\"object\")[0];\n vizElement.style.width = \"1200px\";\n vizElement.style.height = \"827px\";\n let scriptElement = document.createElement(\"script\");\n scriptElement.src = \"https://public.tableau.com/javascripts/api/viz_v1.js\";\n vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 30:"$2f:body"