Message Content Automation

BFFFeed INC (“allyourfeeds”) uses automated systems to recognize patterns on our messaging platform. These patterns help us make your professional communications more efficient and informed. For example, our software does the following:

  1. Looks for specific strings of characters that indicate an emoji to render it as an image.
  2. Looks for text that indicates a web link (e.g. ends with ".com" or similar) to render a preview of the linked page.
  3. When you start typing a name in the addressee field, it attempts to anticipate who the recipient may be and provides auto-complete options.
  4. Looks for mentions of member or company names to add links to their profiles and company pages respectively.
  5. Checks links shared in messages for malicious sites and looks for blacklisted keywords to detect spam.
  6. Scans the text of messages to infer potentially relevant responses (“smart replies”).
  7. Scans attachments for viruses or other harmful code.
  8. Looks for certain characters (e.g. question mark at the end of a message) and context-specific keywords to propose relevant responses ("smart replies").
  9. Looks for mentions of weekdays or dates to see if our software can help you set up a meeting.
Our software sometimes uses these insights together with contextual information. For instance:
  1. If the sender is a recruiter, it's more likely that a message containing certain characters or words is about a job opportunity. Our software may then propose smart replies accordingly.
  2. When a newly opened allyourfeeds account sends frequent messages with certain words and links previously seen in other messages flagged as spam by members, our software may detect these messages as spam.
Although recognizing text patterns in a message is often done at a very basic level (e.g. recognizing a string of characters that make an emoji), our systems sometimes use machine learning to develop and provide more complex functionality.

This essentially means that our analytical models and algorithms are improved over time based on members' usage. For instance, whether and how members use smart replies or our messaging assistant helps refine the replies or other assistance and when they are presented. The refinement of our spam detection models as described above is another example of machine learning based on user feedback.

Please note that with respect to our messaging assistant and similar bots, you have the choice to opt-in or not by adding, mentioning or responding to such bots in a conversation.