The Calendar of Conflict is a data-driven inquiry into the year of 2019 to uncover the hidden points of conflict by analysing the news. A continuously evolving project, it aims to map the important events and trends by filtering through the noise of the news. Using semantic analysis, and keyword tagging, events of conflict have been pulled out from the corpus of news data, which can be accessed via an interactive experience. The intent is to show that news isn’t all that it seems to be, and that we should be aware of the conflicts that happen everyday unseen.
The ways in which we consume news and understand the happenings of our environment have changed with the advent of smartphones and mobile connectivity. The onslaught of news and information is mind numbing, and fatiguing. It is often difficult to process how the world around us really is, and put the things we read into context. In this new paradigm driven by the attention economy, media sources often resort to grabbing the reader’s attention using exaggerated and misleading titles to get them to click and get more views.
The news media has also become increasingly political, and their ability to hold the attention of their audience hasn’t escaped those in power. News cycles are often used by governments and those in power to draw attention away from the bigger issues to escape public scrutiny. Some Indian news media outlets have been vital to the Narendra Modi led government in keeping the public enthralled with divisive issues while those in power get away with undemocratic behaviour. In 2020, the death of a Bollywood actor Sushant Singh Rajput was used to distract public attention away from India’s shrinking economy and poor response to the COVID-19 Pandemic. Many in India truly believe that India is free from any kind of crisis, largely in part due to the lack of news coverage of the issues plaguing India.
The Calendar of Conflict attempts to show the issues India faces with an analytical study of news, highlighting certain events and putting into context the news we read. This project uses a year’s worth of news from 2019 from a largely neutral source, The Hindu to study India from 2019.
The idea of a year’s worth of news is very interesting. Akin to an ice core, it traps with it all the happenings and events from the year. It encapsulates the language, movements, struggles, and the mood of the nation within it. Just like an ice core, studying this slice of history can be very useful in understanding the nation itself.
Select a month and the type of conflict to visualise mentions of conflict. Hover over the red sections to view the corresponding headlines.
A quick recap of the number of mentions of each keyword in 2019.
| Keyword | Mentions |
|---|---|
| Pogrom | 0 |
| Caste Killing | 2 |
| Famine | 35 |
| Police Crackdown | 35 |
| Acid Attack | 56 |
| Police Brutality | 71 |
| Earthquake | 156 |
| Keyword | Mentions |
|---|---|
| Starve | 197 |
| Lynch | 323 |
| Dharna | 813 |
| Bandh | 924 |
| Cyclone | 1031 |
| Theft | 1101 |
| Fraud | 1232 |
| Keyword | Mentions |
|---|---|
| Riot | 1379 |
| Drought | 1443 |
| Clash | 1732 |
| Steal | 1752 |
| Abuse | 1818 |
| Rape | 2456 |
| Violence | 2517 |
| Keyword | Mentions |
|---|---|
| Pollution | 3211 |
| Strike | 3942 |
| Murder | 4888 |
| Flood | 5015 |
| Fight | 6217 |
| Protest | 9655 |
| Kill | 10591 |
In order to analyse the news, I first had to get my hands on a high quality dataset. The Hindu is considered to be largely neutral/leaning left media house, and is known to be of a good editorial standard. I wrote a custom Python script to scrape the archival section of the website which gave me a list links to every article published in 2019. I then wrote another Python script to visit each of their links to scrape the Headline, Location, Date, and the News Article itself. This process took about 13 hours, and the resultant dataset (CSV format) contained around 167700 lines of data, about 245.7MB in size. This was a sizable dataset, and was relatively clean. However it is difficult to work with a dataset of this size, especially when using a mobile computer. Text data processing is a highly CPU intensive task, and more the physical cores, the faster the computation.
I used Orange3, an open-source software to conduct Sentiment Analysis using the Liu Hu process sourced from NLTK. This analysed the main article itself and produced a score which reflects how positive or negative the content is. The Sentiment Analysis is one way to quickly see the ‘mood’ of the news, especially when visualised with a heat map. I took this dataset and used Excel to search for keywords which reflects conflict, such as “Protest”, “Riots”, “Lynch”, “Clash”, and “Strike” amongst others. I also looked up keywords such as “Starve”, “Abuse” and “Deforest” to find other kinds of conflict. On detecting the presence of the particular keyword within the main article, an Excel macro would insert the specific keyword it finds in a new column. These keywords would be the identifiers and tags for each row.
The processing and tagging of text took a fair bit of time due to the size of the dataset. The size of the dataset was a consistent barrier in trying to work with it, which reduced the scope of my explorations with it. To make it easier to work with the dataset, I deleted the main news article itself, which reduced the size of the dataset from 245.7MB to 16.9MB. I used d3js and p5js to read the data and visualise the sentiment analysis and the conflict tagging. The resulting images show how many instances of conflict there really are on any given day.
The various keywords of conflict have been grouped into larger types of conflict, such as interpersonal, political, and environmental, to facilitate easier navigation of the news. Some keywords such as caste have been included, which show a lack of coverage on caste based conflict within the country. This project will have keywords and identifiers continuously added to it with time, to make it more accurate and informative. The intent is to visualise the news to observe patterns, and gain insight about the coverage of different kinds of conflict.
This project first started off with the intent to study the media coverage around divisive events in the country, such as the protests against the Citizen Amendment Act and the subsequent Delhi Pogroms. However, a brief research into the topic raised many more questions than it answered. The Modi-led government has deepened the Right-Left divide in India and this divide has only deepened with the proliferation of the internet and smartphones across India. The advent of state controlled media, and media outlets sympathetic to the cause of the Hindu nationalists have sowed the seeds of disunity amongst communities and families. The rise in the unchecked spread of fake news through social media has blurred the line between fact and fiction. The BJP has spent an exorbitant amount of taxpayer money on using propaganda to sway uninformed and illiterate voters. One on occasion, a virtual rally with the help of 70,000 flat screen TV’s was held in areas of West Bengal devastated by Cyclone Amphan in the middle of global pandemic. The question of identifying what is right and wrong is now more important than ever.
A year’s worth of news also serves as an annual report card, which tells the story of how the country is performing. In a media landscape devastated by biased media coverage and the lack of neutral and analytical reporting, a data-driven look into India can serve as a useful indicator of what happened during the year.
It’s important to point out that everyone is a victim of poor news coverage and improper journalism. Those who have the time, knowledge, and privilege to pay for good journalism and fact-check their news are only slightly better off. The Calendar of Conflict is a data-driven recap of the past that hopes to recontextualise news and help us understand the state of the country.
References
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This map was created by Salil Parekh. The data source for this map was The Hindu. Tools used for this project were Python, Orange3, p5js, d3js, Microsoft Excel. Typefaces used are Inter and Rajdhani. This website is created and hosted on Glitch.