In 2017, We Built an Islamophobia Twitter Tracker. We Saw An 1800% Spike After the NYC Truck Terror Attack.
By: Khuram Zaman
Tags: Islamophobia, Data Visualization, Twitter, Terrorism

In 2017, We Built an Islamophobia Twitter Tracker. We Saw An 1800% Spike After the NYC Truck Terror Attack. Khuram Zaman Follow Jan 15 · 6 min read

At Fifth Tribe, we like to get our designers, developers, and marketers together for company hackathons. During these hackathons, the team gets together and tries to build a prototype using a new tech stack to try and solve a big world problem. Our past hackathons have produced pretty cool projects like this dataset on kaggle on how ISIS uses religious and ideological texts in its propaganda (it got 1st place and a $5,000 in the Kaggle September Dataset Competition). Another project we built was a peer to peer matchmaking platform called Helpugees that helps refugees find pro bono translators, transportation, healthcare, and legal services.

In 2017, we decided to do a company hackathon to track Islamophobia on Twitter. Here’s what we did.

Hackathon Challenge

We started off the hackathon with a design sprint, trying to understand how Islamophobia operated online. We looked at social media, search, video, and websites to try and understand this space. We came up with the following research questions:

What activity is considered Islamophobia?

What are the indicators of Islamophobia?

How does Islamophobia operate on Twitter?

Who are the major influencers?

What sort of tactics do they use?

What hashtags are popular?

Product Vision: NoIslamophobia.com

We felt that the best way to answer this question was to build a simple tracker of Islamophobic terms on Twitter in order to spread awareness of how anti-Muslim sentiment is used on social media to influence policies. We decide to call our prototype “No Islamophobia”, inspired by the “NoHomophobes.com” tracker designed to track anti-gay sentiment on twitter.

User Groups

Who would use this tool? We mapped out the following user groups that we thought could benefit from this tool including data scientists, researchers, community organizations, advocacy organizations, government, and media.

User Stories

We follow agile scrum methodology as our web development process. As part of agile scrum, we created the following user stories:

Setup a github project

Purchase the domain by proxy

Track four keywords: banislam, bansharia, islamistheproblem, civilizationjihad

Create a tweet counter with three views: today, week, and all-time

Setup a database table that tracks each tweet with a timestamp

Display last 10 tweets in real-time

Email signup

Link to Facebook and Twitter

Allow for social sharing via Facebook and Twitter

Create an about page

You’ll notice we focused on hashtags and not people. We wanted to see how the Islamophobia community organized itself online and hashtags are a powerful way for users to organize their messages. Since we’re using the free developer API to pull the tweets, we wanted to get the best bang for our buck so we focused on just a handful of hashtags. We analyzed several hashtags online and felt that these four hashtags were being used most often.

Tech Stack (And A Caveat When Using the Twitter API)

We spent a little time chatting about the tech stack. We settled on using MongoDB, Node.js, Express JS, React JS, Moment JS, AWS Elastic Beanstalk, AWS Lambda, and of course, the Twitter API.

Caveat: Since we used the free developer API to pull the tweets, Twitter places limitation on how much data can be pulled and how frequently. So the data we’re pulling is not by any means a comprehensive view of all the anti-Muslim tweets on Twitter.

Sprint & Retro

After we settled on the user stories and the product roadmap (we use a tool called confluence), we sized the user stories and put them into a two week sprint (we use a tool called jira). Each day, we worked on a handful of tickets and had a prototype up and running within the first 24 hours.

We completed the sprint exactly within two weeks and had the following results:

Goal: 10

Completed: 24

Not Completed: 2

New Tickets: 14

Removed from Sprint: 1

We discovered new features that we thought would be useful to the platform such as:

Made the site mobile responsive (which we did for the most part. It’s not fully 100% mobile responsive

Added a page that allows a user to download the dataset as a .json or .csv file. We figured data scientists would be less interested in our pretty graphs and more interested in playing around with the raw data.

We skinned the site and charts to make it look nicer.

We moved the graph from its own page and instead moved it to the main page

We added google analytics

We also had a host of bugs we needed to fix such as:

A time zone issue on the server that was mis-stating the timestamp for each tweet

The server kept crashing due to a typo in our code which took us a whole day to find. Sigh. So embarrassing.

Cleaned up up the data by removing inconsistent records that had resulted as we were building out the data.

We added a title tag to the dashboard, increased the padding between buttons so they didn’t touch each other, and also fixed the language / images in the social share buttons.

Results + NYC Truck Attack.

The dashboard was live from day one (you can view it here), but it wasn’t fully stable until October 22nd. About a week after we did our soft launch of the tool, a man claiming allegiance to ISIS drove a rented pickup truck down a busy bicycle path near the World Trade Center killing eight people and injuring almost a dozen more. (Source: CNN)

Prior to the attack, we were pulling in just a few hundred tweets a day. After the attack, the number of anti-Muslim tweets jumped into the thousands.

Pre-Attack (October 30th): 255 tweets #banislam

Post-attack (November 1st): 4,848 tweets #banislam

That’s an 1800% increase in people calling for Islam to be banned from the United States.

Many of the tweets were calling for a complete ban of Muslims from immigrating to the US and getting rid of the diversity lotto.

Next Steps

Right now, we’re focusing on data collection and are hoping to build up a dataset of 1,000,000 tweets. We’re at about 500,000+ tweets. After we reach 1,000,000 tweets, we’ll have a large enough dataset that we can start conducting analysis of:

Location : a geographic breakdown of where the tweets are coming from based on user self-disclosure

: a geographic breakdown of where the tweets are coming from based on user self-disclosure Link Origin : an analysis of the original sources of the content that is being shared (with an intent to see if there is any foreign influence on anti-Muslim sentiment here in the US)

: an analysis of the original sources of the content that is being shared (with an intent to see if there is any foreign influence on anti-Muslim sentiment here in the US) Publications : an analysis of which news sources are being cited by this population segment

: an analysis of which news sources are being cited by this population segment Influencers : an analysis of which people are considered thought leaders by Islamophobes

: an analysis of which people are considered thought leaders by Islamophobes Bots : determining if these people are even real or if they’re just bots (which would raise the question of who made them and why?)

: determining if these people are even real or if they’re just bots (which would raise the question of who made them and why?) Images: what images are being shared (with the goal of trying to build out an image database)

Conclusion

The prototype is live at http://noislamophobia.com. Take a look at it and leave a comment below and let us know what you think. What do you like? What do you dislike? How can we improve? If you’re a developer and would like to volunteer your time in helping to build this out, reach out to us.

Tweet us @fifthtribe or email hello@fifthtribe.com.