• This site uses cookies. By continuing to use this site, you are agreeing to our use of cookies. Learn more.

New behavioral analysis tools to fight unwanted content on Twitter

Brink

Staff member
mvp
Oklahoma, USA

Posts
32,265
#1
In March, we introduced our new approach to improve the health of the public conversation on Twitter. One important issue we’ve been working to address is what some might refer to as “trolls.” Some troll-like behavior is fun, good and humorous. What we’re talking about today are troll-like behaviors that distort and detract from the public conversation on Twitter, particularly in communal areas like conversations and search. Some of these accounts and Tweets violate our policies, and, in those cases, we take action on them. Others don’t but are behaving in ways that distort the conversation.

To put this in context, less than 1% of accounts make up the majority of accounts reported for abuse, but a lot of what’s reported does not violate our rules. While still a small overall number, these accounts have a disproportionately large – and negative – impact on people’s experience on Twitter. The challenge for us has been: how can we proactively address these disruptive behaviors that do not violate our policies but negatively impact the health of the conversation?

A New Approach

Today, we use policies, human review processes, and machine learning to help us determine how Tweets are organized and presented in communal places like conversations and search. Now, we’re tackling issues of behaviors that distort and detract from the public conversation in those areas by integrating new behavioral signals into how Tweets are presented. By using new tools to address this conduct from a behavioral perspective, we’re able to improve the health of the conversation, and everyone’s experience on Twitter, without waiting for people who use Twitter to report potential issues to us.

There are many new signals we’re taking in, most of which are not visible externally. Just a few examples include if an account has not confirmed their email address, if the same person signs up for multiple accounts simultaneously, accounts that repeatedly Tweet and mention accounts that don’t follow them, or behavior that might indicate a coordinated attack. We’re also looking at how accounts are connected to those that violate our rules and how they interact with each other.

These signals will now be considered in how we organize and present content in communal areas like conversation and search. Because this content doesn’t violate our policies, it will remain on Twitter, and will be available if you click on “Show more replies” or choose to see everything in your search setting. The result is that people contributing to the healthy conversation will be more visible in conversations and search.

Results

In our early testing in markets around the world, we’ve already seen this new approach have a positive impact, resulting in a 4% drop in abuse reports from search and 8% fewer abuse reports from conversations. That means fewer people are seeing Tweets that disrupt their experience on Twitter.

Our work is far from done. This is only one part of our work to improve the health of the conversation and to make everyone’s Twitter experience better. This technology and our team will learn over time and will make mistakes. There will be false positives and things that we miss; our goal is to learn fast and make our processes and tools smarter. We’ll continue to be open and honest about the mistakes we make and the progress we are making. We’re encouraged by the results we’ve seen so far, but also recognize that this is just one step on a much longer journey to improve the overall health of our service and your experience on it.

Source: Serving Healthy Conversation
 

My Computers

System One System Two

  • Operating System
    Windows 10 Pro 64-bit
    Manufacturer/Model
    Custom
    CPU
    Intel i7-8700K 5 GHz
    Motherboard
    ASUS Maximus X Code Z370
    Memory
    16 GB (8GBx2) G.SKILL TridentZ DDR4 3200 MHz
    Graphics Card(s)
    ASUS ROG-STRIX-GTX1080TI-O11G-GAMING
    Sound Card
    Integrated
    Monitor(s) Displays
    3 x 27" Asus VE278Q
    Screen Resolution
    1920x1080
    Hard Drives
    250GB Samsung 960 EVO M.2,
    256GB OCZ Vector,
    6TB WD Black WD6001FZWX
    PSU
    Seasonic Prime Titanium 850W
    Case
    Corsair Air 740
    Cooling
    Corsair Hydro H115i
    Mouse
    Logitech MX Master
    Keyboard
    Logitech wireless K800
    Internet Speed
    300 Mb/s Download and 30 Mb/s Upload
    Other Info
    Logitech Z625 speaker system,
    Creative F200 webcam,
    HP Color LaserJet Pro MFP M477fdn,
    Linksys EA9500 router,
    Arris SB6190 cable modem,
    APC SMART-UPS RT 1000 XL,
    Lumia 1520 phone
  • Operating System
    Windows 10 Pro
    Manufacturer/Model
    HP Envy Y0F94AV
    CPU
    i7-7500U @ 2.70 GHz
    Memory
    16 GB DDR4-2133
    Graphics card(s)
    NVIDIA GeForce 940MX
    Sound Card
    Conexant ISST Audio
    Monitor(s) Displays
    17.3" UHD IPS touch
    Screen Resolution
    3480 x 2160
    Hard Drives
    512 GB M.2 SSD