Public API
Programmatically interact with Open Measures' data using the Public API
Last updated
Programmatically interact with Open Measures' data using the Public API
Last updated
Open Measures values transparency and proud to offer a free platform in order to help push back on online and offline harm. One of the ways we do this is by via Public API.
Important to note: to mitigate the threat of bad actors, the Public API is rate-limited to 39 requests per day and date-limited such that all visible content is at least six months old. Get in touch with us if you'd like to learn about a non-rate-limited version of the Public API.
Open Source API: https://gitlab.com/openmeasures/backends/openmeasures-api
There''s a link to the API on the Open Source section of the Open Measures website along with more details on the rate-and date-limiting on the Public API:
The API has access to the raw JSON behind all of our front-end tools and can be useful for developers and analysts who want to dive deeper into the data or make more fine-grained queries. The API has three endpoints and has an optional boolean logic query for the Content endpoint.
All API endpoints are hosted at:
Each endpoint in the API has the ability to more advanced queries. This works by leveraging an Elasticsearch query_string_query
(Elasticsearch docs).
This optional parameter is an enum
and tells the API how to interpret the value provided in the mandatory term
parameter.
This parameter allows the user to run one of the following conditions. It is an enum and expects a string parameter:
This parameter will eventually be deprecated. We recommend using querytype
to avoid disruption.
The default for this parameter is False
which means that the API will only search a single term through the document's content field.
NOTE: Setting esquery to True will yield the same results as setting querytype
to query_string
.
A full list of content fields can be found in our open-source code here:
When this is set to True
the API will interpret the value of the term
parameter as a raw Elasticsearch query.
Example:
Here is a link to a Quick Start Code Guide in Colab or Jupyter notebook format for making requests to our API.
We also have a CLI, originally built by a community member, that we forked. Separately, there is the following GitHub repo (https://github.com/cabalcx/smatter/tree/main), also built by community members, that implements a similar wrapper.
We are ALWAYS looking for contributions like this to our technical stack that significantly expand the utility of our data for users.
First, click the Content endpoint and then click “Try it out”.
Choose Telegram as the Site, and then click Execute. It will give you a regular URL link that will have the raw JSON content of your query. The interface will look like this:
If you Curl or go to the URL you will get a raw JSON that will look like the following (certain browsers like Firefox will automatically “prettify” the JSON to make it easier to read):
Example of the same request using curl from the command line and jq to pretty print:
Everything is nested in “hits
” and “_source
” for all of our data.
The Open Measures API is able to be fine-tuned to your exact needs. To show this, we will spell out the steps necessary to pull up to 10k of Guo Wengui’s posts on Gettr as a reference to our post outlining some of the happenings on the alt-tech platform. This post builds upon the information shown in our original API guide. The key element of this advanced usage is using the term query with Elasticsearch query string syntax while setting the es_query field to True.
After heading over to our interactive API docs click the content button:
Content button in Open Measures' interactive API docs tool
Click “Try it out.”
Next to “term” write any interesting word for now.
On “site” select “Gettr.”
Leave all the other settings default for now and click “Execute.”
This will generate a “Request URL” if you copy that link into a new browser window you will be offered a JSON of the data you requested.
JSON is a term indicating a commonly used data format. It contains nested “keys and values”. One way to think about it would be as an org chart spreadsheet where you may have a few classes called keys such as “employee name” or “employee position”. Each of those keys would each have a unique value. Those keys can then be nested in something like the larger department or city that the employee works in. For our data, the JSON has many different fields containing different aspects of the data such as the username, the post itself, the time stamp, and other details. We recommend using a browser like Firefox because it auto-formats the JSON for you. We present the JSON as closely as possible to how it was represented on the native site the data was derived from.
Now that you have some examples of the format of the data you want to explore, dig through it to find the field (or “key”) you want to search under. In our case, we are interested in a field under “uinf” called “username” because we are doing author search. The best way to find the intended field is to look through the JSON results from this /content query.
Prettified JSON blob highlighting the username field.
We are ready to search for the posts written by a specific user on Gettr, now that we know what field corresponds to the username in the JSON.
Back into the interactive API we can now construct our input to the term field in the API. We combine uinf.username with the specific username, in this case “miles”, we are interested in searching using the following syntax: “uinf.username:miles”.
NOTE: For those wishing to learn more about the query language behind these requests check out this documentation or the "Advanced Searching" section of our Kibana guide.
Then we can configure the remaining Open Measures API arguments:
We can raise the “limit” (which is the limit of posts returned) to the 10k point we rate-limit it at.
We can then adjust the “since” field to be farther back.
And finally, and critically for this kind of search, we set the “esquery” boolean item to “true”. This just means that in the term box, instead of accepting a regular search phrase it’s using “Boolean logic” to search through specific fields.
Once your fields look like the following click execute and copy the URL again. It may take a second to load!
Open Measures' interactive point and click API interface
Once you have the JSON opened in a new tab (here’s a direct link to the query we've just demonstrated), you may have to click to expand some of the fields. Most of you’re interested in here will be under: hits > a number > _source. Once there you will see the contents of the message as the field named “txt” as well as other information.
Prettified JSON blog highlighting the txt (or comment body) field
Once you’ve got the hang of searches for all of an author’s post you can experiment with other advanced queries over any of the other fields in any of our data sources such as language, location, links, etc. As always, let us know via email at info@openmeasures.io!
When querytype
is set to content
or boolean_content
the API will search through the default content field for each site. A list of the content field per site can be found in our open-source API code base which is embedded below.
querytype value | Outcome | Example |
---|---|---|
content
This will run an API request to find documents where the input value for term
appears in the content field.
qanon Will return docs where "qanon" appears in the content field.
boolean_content
This will run an API request to find documents where the boolean condition specified in the term
parameter appears in the content field.
qanon AND wwg1wga Will return docs where "qanon" and "wwg1wga" appear in the content field
query_string
This will run an API request using the Elasticsearch query_string_query
syntax. It will search across all fields unless specified in the query.
(channelusername:rtnews) AND (message:russia)
Returns docs where the channelusername
field is set to rtnews
and where the message
field contains russia