This write-up is a companion article to the 13x29 Project — best viewed on Desktop. The best way to go through the project is to first read the introductory parts of this article and then playing with the interactive website.

On 6th September 2018, India took a bold step into the future. The rule of the land outlawed parts of Section 377, thus decriminalizing consensual physical relationship between adults, irrespective of their gender.

Celebrations post Section 377 Vedict | Credits: REUTERS/Francis Mascarenhas

People weren’t celebrating the freedom to have sex. People were celebrating not having to spend every waking moment of their lives thinking they are criminals. It took 158 years, relentlessness of thousands of brave activists and the wisdom of five far-sighted judges for this verdict to materialize.

75% of the world today lives in a country where homosexuality is not a crime, thanks to this decision. Several other countries will look up to India to change their anti-gay laws, and India’s future Alan Turings and Tim Cooks won’t have to end their life to overcome shame. One would expect that such a historic verdict will receive a strong reaction from all walks of life.

Why so much silence?

For three days after the verdict, I was glued to my Twitter feed. My heart swelled reading the tweets and the retweets.

Rainbows and Pride splashed all over my Twitter feed. But then, I noticed something weird. All of the tweets I was seeing were people from the entertainment industry. I wasn’t seeing business people, political leaders or sportsmen who had anything to say about this verdict.

For example, when United States legalized same-sex marriages everywhere in their nation, this is how the White House celebrated.

I was dissapointed that the verdict was being tucked under the bed and not celebrated the way it should be.

Secondly, having followed some amount of Bollywood gossips (thanks Koffee with Karan), I knew who’s whose friend in the Bollywood circles. It seemed like a close group of ‘friends’ were only tweeting about the verdict while the other ‘groups’ were quiet. Network science suggests that lot of traits like smoking, obesity, voting patterns propagate through networks like diseases do. Can LGBTQ-open-mindedness also spread via networks? Do certain sub-networks exists in the groups which are more pro-LGBTQ than others?

This got the Sherlock in me excited. Is there a pattern to this? Who really did comment about the verdict and how did this response vary according to people’s profession?

Identifying 377 Suspects

Without hypothesing much, purely based on curiosity, I decided to analyse 13 different fields with 29 representative influencers from each field — a total of 377 individuals. The fields I initially planned to explore were Politics, Entertainment, Business and Sports. I then subdivided these fields to 13 groups as follows:

Government — The Prime Minister, Cabinet Ministers and Union Ministers Opposition — Leaders from Congress, CPI-M, AAP States — Chief Ministers of the 29 States Faith — Religious leaders, organizations, activists Journalism — Journalists, Editors, Writers Law — Lawyers Sports — Cricketers, Olympics Medal Winners Bollywood — Folks from Hindi Film Industry Music — Singers Creativity — Choreographers, Fashion Designers, Writers, Music Directors YouTube — Popular on Indian YouTube Entrepreneurship —Startup Founders, VCs Forbes 30 Under 30 — Folks listed by Forbes’ 30 Under 30.

You can have a look at the people included in the sample set here: https://twitter.com/iashris/lists

I based my sampling on the number of followers — a proxy to measure a person’s influence. For people with similar followers, I picked people at random, which I admit doesn’t zero the sampling errors but still gives a big picture view.

What did they say?

I was interested in what people were talking around the time of the verdict, so I decided to choose my analysis window from midnight 5th September to midnight 7th Septmber — centered around the day of the verdict of 6th.

I used the Tweepy library to write a Python script that fetched all the tweets posted by these 377 individuals durig these three days. Twitter doesn’t have an API to access tweets between a date, so I wrote a script that recursively pushes back the start_date till the needed tweets are fetched. Along with the tweets, I also extracted the number of followers, their profile images, hashtags used and their retweets. I did a keyword analysis to separate the tweets into ‘targeted’ and ‘non_target’ — depending on whether the tweet contained hashtags or text with words like 377, pride, lgbt, supreme court or love is love.

A JSON file was obtained for every person with their tweets and hashtags

Networkifying the Groups

I was not only interested in who was saying what but also if there was some kind of connection between people saying similar things. For this, I had to know how do people in each group are connected to each other.

The data I was looking for was — who follows whom. Twitter provides this information through their show friendship API. I obtained the pairs of all possible combinations of people in a group and obtained their ‘friendship’ data.

CSV containing who-follows-whom data

Visualizing the Network and the Hotspots

Once I had the two parts available — the next step was to visualize it. The fun part begins! I used this amazing force directed algorithm library to trigger a physics based simulation which clusters together people who follow each other and isolates those who aren’t as conencted with others.

To indicate the ones who reacted to the Verdict — let’s call them ‘Reactors’ — I made them stand out from the rest by giving a rainbow label. The ones who did not react — let’s call them ‘Non Reactors’ — I made their thumbnails smaller and greyscaled.

Aannnd…. Drumrolls!