Freedom2hear acts as your emotionally intelligent content filter, protecting your people from hate.
We specialise in emotion-based AI content moderation, identifying the context of online conversations and preventing the widespread distribution of flagged posts across your channels, without impairing any author’s freedom to publish.
About usFreedom2hear equips you with powerful tools to moderate and manage online interactions effectively, ensuring safer, healthier digital spaces for everyone.
Manage flagged content seamlessly through our intuitive Moderation Hub. Review flagged posts, and take action in real time to protect your community from harmful content.
Measure the toxicity levels across your channels with advanced analytics tools. Gain insights into patterns of harmful behaviour and make data-driven decisions to improve safety.
Tailor moderation to your needs and customise your strategy to meet the unique needs of your community by targeting specific types of toxicity, and allowing or banning unique keywords.
In recent years, online abuse targeting female athletes has escalated dramatically, posing significant risks to their well-being and professional careers. These disturbing trends underscore the urgent need for stronger protections for athletes, particularly female athletes, who are disproportionately impacted by such abuse. The situation highlights the growing importance of creating safer, more inclusive online spaces within the world of sports.
The Netflix series Adolescence has ignited crucial discussions about the profound influence of social media on today's youth. This article highlights the urgent need for responsible and psychologically informed content moderation. Drawing from real-world data, social and emotion psychology theories, it underscores how advanced moderation technologies, like those built into our social media solution, can help prevent harm by identifying nuanced patterns of toxicity, decoding hidden language, and supporting healthier digital environments.
The blog post outlines the challenges in benchmarking emotional intelligence in AI systems, highlighting issues such as subjective scene settings, ambiguous labelling, and hidden assumptions that often lead to inconsistent evaluations. It calls for an interdisciplinary, nuanced approach - one that not only measures outcomes but also examines the reasoning behind responses, and their consistency - to better capture the complexities of human emotion in real-life scenarios.
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