Query: mother, father, mom, dad
Sample: Longitudinal sample


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Read more about Jason J. Jones Identity Trends V1.

Questions You May Have

You are seeing the prevalence of American Twitter users whose profile bio contains each token for each year 2015-2020.

Think of this as the "popularity" of a word within American Twitter users' profile bios. Precisely, this is the value 10,000 * (count of profiles containing word / count of ALL profiles) rounded to the nearest integer. You should interpret prevalence as the number of users per 10,000 unique users who had a profile containing token. It is nice to work with prevalence, because people (even you) are bad at fractions and decimals. Most tokens are NOT used by most users, so all incidences are small compared to the total user count.
American Twitter users
Users are found in randomly sampled public tweets from the Twitter API. The users counted here are those who have tweeted, had their tweet sampled, and whose self-entered 'location' field was a US state, city, area code or other likely indicator of a US geographical location.
Profile Bio
Users are prompted at sign-up to 'Describe themselves in 160 characters or less.' Users can edit their bio at any time. This field is called the 'description' in the Twitter API's User object.
Think of this as a word. Precisely, each token is a character string obtained by splitting up a bio. The tokenization is a simple string split on the regular expression \b, which is a word boundary. Many tokens are words like you would find in the dictionary, but not all. The JJJIT-V1 dataset provides counts for many tokens, so you will see abbreviations, numbers, and other things. Tokens whose prevalence is less than 1 in 10,000 are not included in the dataset.
Cross-sectional sample
All unique users observed as they are active. This is a good sample to use to study the population of active Twitter users at a time. You can answer the question: What are American users like on the Twitter platform?
Longitudinal sample
Only those users who were active and sampled in every year 2015-2020. This is a good sample to use to study how Twitter users change over time. You can answer the question: What words (on average) are Twitter users choosing to add, remove or keep within their bios?

For a quick, one-page introduction, read the About page.

For more depth and to see a practical application, read my peer-reviewed research article with Nick Rogers titled Using Twitter Bios to Measure Changes in Self-Identity: Are Americans Defining Themselves More Politically Over Time?

Or read my preprint with Irissa Cisternino titled Societal Pressures, Safety, and Online Labeling - Investigating LGBTQ Self-Identification in an Online Space.

This website is first iteration of the Identity Trends component of the LOPS - Longitudinal Online Profile Sampling project by Dr. Jason Jeffrey Jones.

Jason J. Jones is me: Dr. Jason Jeffrey Jones.

I am the director of the Computational Social Science of Emerging Realities Group. We use the methods of computational social science to study human behavior at scale.

But why is your name in the title? 1) It makes the title unique, easier-to-remember and search for. 2) My favorite video game of all time is Sid Meier's Alpha Centauri, and this anecdote about the name has stuck with me every since I read about it.

Yes, I do have a public Twitter account with an American location.


Data Table

Jason J. Jones Identity Trends V1 tabular results for query = mother, father, mom, dad; Longitudinal sample
Token Year Prevalence