The SMAT 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 recent post outlining some of the sketchy past and present of Gettr. This post builds upon the information shown in our original API guidance blog post. 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 SMAT’s 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.
NOTE: JSON is just a term for a type of data format commonly used on the web. It contains nested “keys and values”. One way to think about it would be in a workplace table you would have a few classes called keys such as “employee name” or “employee position” that would each have a unique value. They can then be nested in something like the larger department or city they work 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 posted, and other details. We recommend using a browser like Firefox because it auto-formats the JSON for you. We present the JSON as close to as exact as it was represented on the native site the data was crawled 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. At the moment SMAT doesn’t have data field descriptions and so the best way for finding 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.
Then we can configure the remaining SMAT 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!
SMAT’s 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 discussed!), 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
Quick Start Guide
Here is a link to a Quick Start Code Guide in Colab or Jupyter notebook format for making requests to our API.
As well, here are a few field names to get you up and running but there are many more interesting fields in each dataset such as likes, external links, even language, and user-chosen locations in some datasets.
Table of key field names for various sites
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 on Twitter or elsewhere what you find!