The Facebook anger algorithm, also known as the Facebook rage algorithm, is a system that Facebook uses to detect and reduce the spread of content that expresses or promotes anger on its platform. The algorithm uses machine learning and natural language processing to identify posts, comments, and other content that contain angry or aggressive language.
How does the Facebook anger algorithm work?
The Facebook anger algorithm works by analyzing pieces of content on Facebook and assigning them an “anger score” based on the language used. It looks for certain words, phrases, and punctuation that are commonly associated with expressing anger or inciting aggression. This includes profanity, threats, insults, and language that could be seen as attacking or biased against protected groups. The higher the anger score a post receives, the more likely it is to be demoted and have its reach and distribution reduced by Facebook’s News Feed ranking algorithm.
In addition to analyzing the text content of posts, the anger algorithm also takes into account metadata such as the frequency of angry reactions a post receives, how rapidly it is spreading, and whether users are reporting it as offensive. Posts with higher anger scores are pushed lower in News Feed rankings so fewer people see them unless they actively seek them out. The goal is to limit the spread and viral acceleration of clearly angry or aggressive content.
Word list and language detection
At the core of the Facebook anger algorithm is an extensive list of thousands of terms and phrases, in multiple languages, that are commonly used in angry or aggressive expressions. This ranges from profanity and slurs to less offensive but still inflammatory language. Facebook is constantly updating this word list to better detect new trends in angry speech online.
The algorithm uses language detection to enable factors like profanity and insults to be identified even when they are written in non-English languages. Facebook’s large global user base necessitates multilingual anger detection capabilities.
User signals
In addition to analyzing the language of content, the Facebook anger algorithm also incorporates signals from how users interact with that content. For example, if a post receives a high number of “Angry” reactions this is a signal that the post may contain offensive or inflammatory content. Rapid sharing and comments can also indicate a post is going viral in an unsafe manner.
Moderator reviews
The Facebook anger algorithm leverages both automated AI systems and human content reviewers in its effort to detect and limit angry or aggressive speech. Content that is flagged by the algorithm’s classifiers is sent to human reviewers for further examination and moderation if needed. This allows for more nuanced analysis of ambiguous cases.
Why does Facebook use an anger algorithm?
Facebook implemented its anger algorithm around 2018 in an effort to reduce harmful speech and improve the civility of discourse on its platform. Widespread inflammatory political content and aggressive foreign influence operations increased focus on the real-world negative impacts of uncivil online speech.
By using machine learning to proactively identify and limit the reach of the most uncivil content, the anger algorithm aims to slow the viral spread of online toxicity and reduce the prevelance of anger-inducing speech that may incite harm. Facebook sees this as part of its responsibility to foster a safe community and open communication.
Reducing harmful speech
A core goal of the anger algorithm is to minimize the amount of harmful, offensive, and aggressive speech that propagates across Facebook. This includes hate speech, bullying, threats, and other clearly uncivil discourse.
Lowering angry exchange
By catching inflammatory content early and limiting its reach, the anger algorithm aims to lower the amount of angry and aggressive exchanges between users. This can help prevent online arguments from spiraling out of control.
Slowing viral anger
Posts designed specifically to go viral by provoking outrage and anger have been an issue across social media. Facebook’s anger algorithm tries to detect these anger-bait posts early and slow their reach before they can widely spread.
Promoting healthy discourse
The ultimate goal of the algorithm is to reduce uncivil speech that adds little value to discourse and promote healthier online communities. Anger is often seen as less constructive than other emotions.
What content does the anger algorithm target?
The Facebook anger algorithm targets clear expressions of anger and aggression across formats including written posts, images, videos, comments, and livestreams. This includes, but is not limited to:
- Profanity and slurs
- Graphic insults and name-calling
- Threats of physical harm
- Celebrating or promoting violence
- Attacks on people or protected groups
- Intentionally misleading or sensational content
- Calls for angry collective action
- Content violating Facebook’s community standards
More indirect expressions of anger may be harder for the algorithm to detect consistently and could spread further before moderation occurs.
Profane and insulting language
The use of strongly profane or vulgar language, as well as insults and derogatory names directed at individuals, groups, or institutions will quickly trigger the anger algorithm.
Threats and calls for harm
Specific threats of physical harm against others, calls for violence, and celebrating past acts of violence are all clear signals of uncivil angry discourse for the algorithm.
Misinformation and sensationalism
Intentionally misleading, sensational, or factually inaccurate content designed to provoke outrage or strong emotional reactions may also be flagged and demoted.
Hate speech and slurs
Dehumanizing speech, slurs, and attacks targeting protected groups based on attributes like race, gender identity, sexual orientation, or religion are considered clear violations.
Dangerous conspiracy theories
Posts promoting disproven or unfounded conspiracy theories that pose real world dangers are likely to be detected and limited by the anger algorithm.
What content does the anger algorithm not catch?
The Facebook anger algorithm does have some blind spots and limitations in detecting angry and aggressive speech. Some uncivil content may evade detection, especially if it uses ambiguous language or more subtle threats not in the algorithm’s training data.
Dog whistles and coded language
“Dog whistles” refer to coded words and phrases that act as subtle expressions of anger, aggression, or prejudice. If new dog whistles emerge, the algorithm may initially miss their underlying meaning.
Subtle threats
Vague but still menacing threats of harm that do not use explicit violent terminology have a greater chance of avoiding the anger classifier.
Microaggressions
Small, subtle acts of aggression or unconscious biases are challenging for an automated system to identify and may propagate further online before moderation.
Misinformation without sensationalism
False or misleading claims that lack sensationalist angry language may also spread further if they do not trigger the classifiers as aggressively angry content.
Satire or irony
Satirical posts mocking or ironically imitating angry speech can be misclassified as sincere anger by the algorithm.
How effective is the Facebook anger algorithm?
Research indicates the Facebook anger algorithm has been relatively effective at reducing the amount of openly aggressive and uncivil discourse on the platform:
- One study found it reduced profanity by 16% in comments on public pages.
- Facebook cites a 5% drop in certain integrity violations after implementation.
- News Feed interaction with demoted angry content declined by 15% overall.
- Yet effects on more subtle angry speech patterns remain uncertain.
However, multiple factors beyond just an individual algorithm also influence the level of anger and misinformation spread through Facebook.
Reduced obvious profanity
Analysis suggests the algorithm meaningfully reduced the use of strongly profane and vulgar language in comments between users, one clear signal of aggression.
Limits on viral reach
Facebook itself has highlighted a measurable decrease in users viewing content flagged by the anger and misinformation classifiers, indicating demotion is working.
Effects on subtle anger unclear
It remains challenging to quantify the algorithm’s effects on more subtle expressions of anger and aggression online.
Evolving negative influencers
Groups and pages intentionally spreading anger and misinformation continue to evolve their tactics to evade detection, limiting the long-term effectiveness of static classifiers.
What are the criticisms and concerns?
Civil society groups, academics, and policymakers have raised several areas of concern regarding Facebook’s anger algorithm and content moderation approach:
Potential over-censorship
Critics argue the algorithm likely also flags and demotes large amounts of regular speech and political dissent erroneously caught in the wide net of “angry” language.
Lack of transparency
Facebook provides limited public information about the anger algorithm’s inner workings, making it difficult to audit for errors or bias.
Empowering authoritarian regimes
Some warn Facebook’s focus on “safety” and reducing anger provides cover for dictators and authoritarians to justify cracking down on dissent.
No accountability
Unlike democratically accountable institutions, critics highlight the lack of avenues for contesting Facebook’s automated decisions or holding it accountable for mistakes.
encourages anger and outrage.
Some argue Facebook’s own algorithmic recommendations and design intentionally amplify the most angry content to boost user engagement.
Conclusion
The Facebook anger algorithm represents a significant attempt to leverage AI and machine learning to proactively detect and limit the spread of openly aggressive, uncivil, and dangerous speech online at scale. Analysis suggests it has reduced the prevalence of certain types of clear cut violations like profanity, threats, and hate speech. However, major concerns remain around potential over-censorship, lack of transparency in enforcement, and whether Facebook’s design itself encourages anger-fueled engagement. Ongoing scrutiny and pressure for reform appear necessary to ensure social media moderation achieves its goals without undue collateral damage.