#SMTLive Recap: Managing Your Brand’s Social Media Over the Holidays

It’s Thanksgiving week in the United States, which means we’re officially in the thick of the holiday season. For social media marketers, this can mean a lot of chaos. In order to make said chaos a bit more organized, we wanted to ask our #SMTLive community on Twitter about how they prepare for the holidays on social.
Firstly, we wanted to see if anyone hadn’t gotten started yet.
Q1 – Poll: Do you have a social media plan in place for the holidays? #SMTLive
— Social Media Today (@socialmedia2day) November 26, 2019
It turns out most people actually have gotten started, proving that those of us working in social have learned the hard way that procrastination is not your friend at this time of year.
Fun! What type of content do you plan to produce at the holiday party? #SMTLive
— Social Media Today (@socialmedia2day) November 26, 2019
We’re wearing ugly sweaters and are planning to take plenty of photos, videos, and even going to try going LIVE on Facebook & Instagram!
— 5 Fold Marketing (@5FoldMarketing) November 26, 2019
Holiday parties seem like a great opportunity for behind the scenes content and experimenting with Instagram/Facebook Live!
Q2 – How do you think your social media strategy should be managed differently than usual over the holidays? #SMTLive pic.twitter.com/YYJmcprM0V
— Social Media Today (@socialmedia2day) November 26, 2019
With so many events to prep for during the holiday season, plus employee time off, this time of year often provokes a change in workflow.
A2 – Our social media strategy will be geared more towards company culture and lighthearted fun rather than sales and informational pieces. #SMTLive pic.twitter.com/jtQMy0Xz4P
— 5 Fold Marketing (@5FoldMarketing) November 26, 2019
Judging by that gif, it looks like they’re already getting started.
A2. You have to be ready to scale up/down over the holiday. Lots of posts & customer service while people are shopping; then a calmer phase over the holiday week itself, with a less salesy focus. AND you need to factor in holiday time for your team. #SMTLive
— Corinna Keefe (@corinna_keefe) November 26, 2019
The holidays provide a change of pace for many, including @corinna_keefe.
Q3 – What is the most annoying thing a brand can do over the holidays on social? #SMTLive pic.twitter.com/BB6dCaL9VJ
— Social Media Today (@socialmedia2day) November 26, 2019
This time of year provides a multitude of content creation opportunities, which sounds like a good thing. And it is… until you accidentally go overboard and overwhelm your audience. Here’s what #SMTLive thinks you should avoid specifically:
A3 – POST TOO MUCH. I am more than likely going to unfollow a brand if I see multiple posts in my feed on the same day. (This does not apply for Black Friday sales deals) ????️ #SMTLive
— 5 Fold Marketing (@5FoldMarketing) November 26, 2019
With so many different holidays and, especially for B2C brands, shopping opportunities, it’s easy to overshare.
This is why it’s a good idea to only follow the brands you’re super interested in seeing! Weed out the ones you don’t care about ????
— 5 Fold Marketing (@5FoldMarketing) November 26, 2019
@5FoldMarketing’s tip is great for the average user and marketer alike. If you only follow brands you’re really into, you probably won’t be annoyed by their posts. And, if you’re a marketer, you’ll get more inspiration from the brands you love than the ones you feel lukewarm about.
A3: Probably be too salesy. It’s a delicate balance and selling too much can turn people off. Also the sudden mass emails from companies on #BlackFriday can be pretty annoying. #SMTLive
— Hannah Richards (@actPRHannah) November 26, 2019
But this begs the question: Would you care about three emails in a day from your favorite brand? Honestly… most likely. Anyone who checks email over the holidays has probably felt a little bogged down by all the noise.
A3 I appreciate the sales emails over the holidays IF they are in fact good deals/sales! Annoying: an email ‘lookbook’ of your products that are full price… No thanks 🙂 #SMTLive
— Robin Selvy Re (@RobinSelvyRe) November 26, 2019
But @RobinSelvyRe came back with a great point: If you see three useful emails about three separate deals, that probably wouldn’t be so annoying, especially from your favorite brand.
Next question… Q4 – How can you take advantage of the holiday season to boost your social engagement and brand awareness? #SMTLive (Think of specific examples.) pic.twitter.com/5RejBoBTiJ
— Social Media Today (@socialmedia2day) November 26, 2019
While the holidays might not be an excuse to mindlessly overshare, they do provide a unique opportunity for social success. A few users during our chat summed up one of the most unique opportunities the season has to offer:
Showing thanks to your customers is huge. Thanks for sharing! #SMTLive
— 5 Fold Marketing (@5FoldMarketing) November 26, 2019
Agreed. It also stands out more than any product ever will. People want human connection out of social media. #SMTLive
— Hannah Richards (@actPRHannah) November 26, 2019
The next few months are a great time to show some authenticity, as ironic as that may be. Behind the scenes content and a few holidays in particular can really show what your brand values.
One brand whose values get brought up pretty often in discussions on social came up in our final question of the chat:
A5 – @Starbucks holiday cups. Genius. Simple. Timeless. It was an in-person deliverable that worked both in-store as well as online. We’re big fans. pic.twitter.com/hLeEnjvR0S
— 5 Fold Marketing (@5FoldMarketing) November 26, 2019
Whether you’re creating a content calendar by Christmas tree light or filming an Instagram Live at your office holiday party, we hope these tips from #SMTLive keep you merry and bright this holiday season!
Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK
First seen at developers.facebook.com
Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

Updated July 18: Developers and advertising partners may be required to share information on their app’s privacy practices in third party app stores, such as Google Play and the Apple App Store, including the functionality of SDKs provided by Meta. To help make it easier for you to complete these requirements, we have consolidated information that explains our data collection practices for the Facebook and Audience Network SDKs.
Facebook SDK
To provide functionality within the Facebook SDK, we may receive and process certain contact, location, identifier, and device information associated with Facebook users and their use of your application. The information we receive depends on what SDK features 3rd party applications use and we have structured the document below according to these features.
App Ads, Facebook Analytics, & App Events
Facebook App Events allow you to measure the performance of your app using Facebook Analytics, measure conversions associated with Facebook ads, and build audiences to acquire new users as well as re-engage existing users. There are a number of different ways your app can use app events to keep track of when people take specific actions such as installing your app or completing a purchase.
With Facebook SDK, there are app events that are automatically logged (app installs, app launches, and in-app purchases) and collected for Facebook Analytics unless you disable automatic event logging. Developers determine what events to send to Facebook from a list of standard events, or via a custom event.
When developers send Facebook custom events, these events could include data types outside of standard events. Developers control sending these events to Facebook either directly via application code or in Events Manager for codeless app events. Developers can review their code and Events Manager to determine which data types they are sending to Facebook. It’s the developer’s responsibility to ensure this is reflected in their application’s privacy policy.
Advanced Matching
Developers may also send us additional user contact information in code, or via the Events Manager. Advanced matching functionality may use the following data, if sent:
- email address, name, phone number, physical address (city, state or province, zip or postal code and country), gender, and date of birth.
Facebook Login
There are two scenarios for applications that use Facebook Login via the Facebook SDK: Authenticated Sign Up or Sign In, and User Data Access via Permissions. For authentication, a unique, app-specific identifier tied to a user’s Facebook Account enables the user to sign in to your app. For Data Access, a user must explicitly grant your app permission to access data.
Note: Since Facebook Login is part of the Facebook SDK, we may collect other information referenced here when you use Facebook Login, depending on your settings.
Device Information
We may also receive and process the following information if your app is integrated with the Facebook SDK:
- Device identifiers;
- Device attributes, such as device model and screen dimensions, CPU core, storage size, SDK version, OS and app versions, and app package name; and
- Networking information, such as the name of the mobile operator or ISP, language, time zone, and IP address.
Audience Network SDK
We may receive and process the following information when you use the Audience Network SDK to integrate Audience Network ads in your app:
- Device identifiers;
- Device attributes, such as device model and screen dimensions, operating system, mediation platform and SDK versions; and
- Ad performance information, such as impressions, clicks, placement, and viewability.
First seen at developers.facebook.com
Enabling Faster Python Authoring With Wasabi

This article was written by Omer Dunay, Kun Jiang, Nachi Nagappan, Matt Bridges and Karim Nakad.
Motivation
At Meta, Python is one of the most used programming languages in terms of both lines of code and number of users. Everyday, we have thousands of developers working with Python to launch new features, fix bugs and develop the most sophisticated machine learning models. As such, it is important to ensure that our Python developers are productive and efficient by giving them state-of-the-art tools.
Introducing Wasabi
Today we introduce Wasabi, a Python language service that implements the language server protocol (LSP) and is designed to help our developers use Python easier and faster. Wasabi assists our developers to write Python code with a series of advanced features, including:
- Lints and diagnostics: These are available as the user types.
- Auto import quick fix: This is available for undefined-variable lint.
- Global symbols autocomplete: When a user types a prefix, all symbols (e.g. function names, class names) that are defined in other files and start with that prefix will appear in the autocomplete suggestion automatically.
- Organize Imports + Remove unused: A quick fix that removes all unused imports and reformats the import section according to pep8 rules. This feature is powered by other tools that are built inside Meta such as libCST that helps with safe code refactoring.
- Python snippets: Snippet suggestions are available as the user types for common code patterns.
Additionally, Wasabi is a surface-agnostic service that can be deployed into multiple code repositories and various development environments (e.g., VSCode, Bento Notebook). Since its debut, Wasabi has been adopted by tens of thousands of Python users at Meta across Facebook, Instagram, Infrastructure teams and many more.
Figure 1: Example for global symbols autocomplete, one of Wasabi’s features
Language Services at Meta Scale
A major design requirement for language services is low latency / user responsiveness. Autocomplete suggestions, lints and quickFixes should appear to the developer immediately as they type.
At Meta, code is organized in a monorepo, meaning that developers have access to all python files as they develop. This approach has major advantages for the developer workflow including better discoverability, transparency, easier to share libraries and increased collaboration between teams. It also introduces unique challenges for building developer tools such as language services that need to handle hundreds of thousands of files.
The scaling problem is one of the reasons that we tried to avoid using off-the-shelf language services available in the industry (e.g., pyright, jedi) to perform those operations. Most of those tools were built in the mindset of a relatively small to medium workspace of projects, maybe with the assumptions of thousands of files for large projects for operations that require o(repo) information.
For example, consider the “auto import” quick fix for undefined variables. In order to suggest all available symbols the language server needs to read all source files, the quick fix parses them and keeps an in-memory cache of all parsed symbols in order to respond to requests.
While this may scale to be performed in a single process on the development machine for small-medium repositories, this approach doesn’t scale in the monorepo use case. Reading and parsing hundreds of thousands of files can take many minutes, which means slow startup times and frustrated developers. Moving to an in-memory cache might help latency, but also may not fit in a single machine’s memory.
For example, assume an average python file takes roughly 10ms to be parsed and to extract symbols in a standard error recoverable parser. This means that on 1000 files it can take 10 seconds to initialize which is a fairly reasonable startup time. Running it on 1M files would take 166 minutes which is obviously a too lengthy startup time.
How Wasabi Works
Offline + Online Processing:
In order to support low latency in Meta scale repositories, Wasabi is powered by two phases of parsing, background processing (offline) done by an external indexers, and local processing of locally changed “dirty files” (online):
- A background process indexes all committed source files and maintains the parsed symbols in a special database (glean) that is designed for storing code symbol information.
- Wasabi, which is a local process running on the user machine, calculates the delta between the base revision, stack of diffs and uncommitted changes that the user currently has, and extracts symbols only out of those “dirty” files. Since this set of “dirty” files is relatively small, the operation is performed very fast.
- Upon an LSP request such as auto import, Wasabi parses the abstract syntax tree (AST) of the file, then based on the context of the cursor, creates a query for both glean and local changes symbols, merges the results and returns it to the user.
As a result, all Wasabi features are low latency and available to the user seamlessly as they type.
Note: Wasabi currently doesn’t handle the potential delta between the revision that glean indexed (happens once every few hours) and the locally base revision that the user currently has. We plan on adding that in the future.
Figure 2: Wasabi’s high level architecture
Ranking the Results
In some cases, due to the scale of the repository, there may be many valid suggestions in the set of results. For example, consider “auto import” suggestions for the “utils” symbol. There may be many modules that define a class named “utils” across the repository, therefore we invest in ranking the results to ensure that users see the most relevant suggestions on the top.
For example, auto import ranking is done by taking into account:
- Locality:
- The distance of the suggested module directory path from the directory paths of modules that are already imported in this file.
- The distance of the suggested module directory path from the current directory path of the local file.
- Whether the file has been locally changed (“dirty” files are ranked higher).
- Usage: The number of occurrences the import statement was used by other files in the repository.
To measure our success, we measured the index in the suggestion list of an accepted suggestion and noted that in almost all cases the accepted suggestion was ranked in one of top 3 suggestions.
Positive feedbacks from developers
After launching Wasabi to several pilot runs inside Meta, we have received numerous positive feedbacks from our developers. Here is one example of the quote from a software engineer at Instagram:
“I’ve been using Wasabi for a couple months now, it’s been a boon to my productivity! Working in Instagram Server, especially on larger files, warnings from pyre are fairly slow. With Wasabi, they’re lightning fast 😃!”
“I use features like spelling errors and auto import several times an hour. This probably makes my development workflow 10% faster on average (rough guess, might be more, definitely not less), a pretty huge improvement!”
As noted above, Wasabi has made a meaningful change to keep our developers productive and make them feel delightful.
The metric to measure authoring velocity
In order to quantitatively understand how much value Wasabi has delivered to our Python developers, we have considered a number of metrics to measure its impact. Ultimately, we landed on a metric that we call ‘Authoring Velocity’ to measure how fast developers write code. In essence, Authoring Velocity is the inverse function of the time taken on a specific diff (a collection of code changes) during the authoring stage. The authoring stage starts from the timestamp when a developer checks out from the source control repo to the timestamp when the diff is created. We have also normalized it against the number of lines of code changed in the diff, as a proxy for diff size, to offset any possible variance. The greater the value for ‘Authoring Velocity,’ the faster we think developers write their code.
Figure 3: Authoring Velocity Metric Formula
The result
With the metric defined, we ran an experiment to measure the difference that Wasabi brings to our developers. Specifically, we selected ~700 developers who had never used Wasabi before, and then randomly put them into two independent groups at a 50:50 split ratio. For these developers in the test group, they were enabled with Wasabi when they wrote in Python, whereas there was no change for those in the control group. For both groups, we compare the changes in relative metric values before and after the Wasabi enablement. From our results, we find that for developers in the test group, the median value of authoring velocity has increased by 20% after they started using Wasabi. Meanwhile, we don’t see any significant change in the control group before and after, which is expected.
Figure 4: Authoring Velocity measurements for control and test groups, before and after Wasabi was rolled out to the test group.
Summary
With Python’s unprecedented growth, it is an exciting time to be working in the area to make it better and handy to use. Together with its advanced features, Wasabi has successfully improved developers’ productivity at Meta, allowing them to write Python faster and easier with a positive developer experience. We hope that our prototype and findings can benefit more people in the broader Python community.
To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.
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