Tips for Investigating Hate Crimes and Violence When Government Data Sources Fail

2. Prepare for Preconceived Ideas to be Challenged

Your database could surprise you out of preconceived notions, but you cannot exclude or include data to confirm your own preconceptions without rendering your work useless.

3. Beware of Typos

It sounds like a small issue, but one or two typos can make a worksheet unsearchable. For example, under region, Uttar Pradesh spelled as “Utar Pradesh” meant that it would pull up as a different state while trying to filter search results. Copy editing is dull work, but it meant that I and others could easily analyze the data.

4. Delete Columns That Do Not Have Enough Data

Initially, I had columns documenting ethnicity of victim, sexual orientation, and the number of passive observers of the crime. In the case of the first field, the information became so repetitive as to be redundant. I realized that the nationality of the vast majority in the attacks was Indian, and that filtering for this information would not be of use. In the case of the second two fields, the information was available so rarely that the columns were no longer useful, because the available data would not be representative. Focus on the data that is available within your set.

5. The Need for Footnotes

While it makes sense in a database to refer to the Vishva Hindu Parishad [a Hindu religious organization] as the VHP, I often had to explain my acronyms and shorthand to others. Use a separate sheet or the comments tool to store footnotes about acronyms or links to explainers. This adds clarity so that anyone using the dataset will understand, without “breaking” your filters or cluttering your data set.

6. The Value of Brevity

In a database, I realized some of the easiest searches happen with columns that are in the yes/no format. For instance, did the police file a First Information Report (FIR, the equivalent of a police complaint): yes, or no? Did the police file an FIR against the victim or the perpetrator? By inputting the answers to such questions, I was able to see some unexpected results, for example: police filed FIRs against both the victim and perpetrator in 13% of cases.

Image: Screenshot

Filling the Void

The list of information that India’s current government withholds from the public keeps getting longer. I can envision independent journalist teams collecting data on topics like how many health workers have been infected with COVID-19, the number of rural child-care social workers who left their jobs during the pandemic, the number of school dropouts, the closure of small- and medium-sized enterprises, the number of attacks on Right to Information activists, and many other stories that require careful monitoring and data that may not be forthcoming from official sources.

In response, other journalists, academics, and activists in India have started creating databases because of the growing void in official data on crucial issues. For example, Article 14 created a Decade of Darkness database project, in which it documents the number of sedition cases filed in the past 10 years. An increasingly common intimidation tactic in India involves the current government accusing journalists and activists of sedition and being “anti-national.”

Conclusion

My hope is that this paper helps journalists think about how they define the questions they want to answer, how they collect the data, and how they report on it.

Work on the hate crimes tracker is by no means complete: I intend to take it online and invite other journalists to gather and catalogue information. It currently only includes reports from English-language outlets, and could be dramatically expanded if we were to include regional language reports too.

The risk to quality of information in crowdsourced projects like these lies in duplication of entries, authentication of the reports, and implicit bias in the interpretation of results. With full awareness of those risks – and the security risks to those who try to keep tallies – I believe it is still worth trying.

This article was originally published by the Reuters Institute for the Study of Journalism. GIJN added a small amount of additional material from the author, including from the full paper.  

Additional Resources

Interpreting Data: Tips to Make Sure You Know How to Read the Numbers

How One Mexican Data Team Uncovered the Story of 4,000 Missing Women

How Bellingcat and Forensic Architecture Teamed Up with Colombia’s Cerosetenta to Map Police Violence

Rachel Chitra is a finance journalist and special correspondent for the Times of India with a passion for using data to tell human stories. She has previously worked for Reuters, New Indian Express, and Deccan Chronicle. Her project for the Reuters Institute looked at the documentation of lynching and rape of Muslims and Dalits in India.