Social media platforms may be failing to protect African-language users equally, as gaps in artificial intelligence (AI) training data and language capabilities raise concerns about content moderation and online safety.
A 2025 analysis by the Institute for Strategic Dialogue found that TikTok removed or restricted the visibility of only five of 16 reported comments in a study examining how Amharic text was used to evade hate-speech moderation.
The findings raised concerns about the platform's ability to identify harmful content when it appears alongside less-supported languages.
In Nigeria, reported use of Yoruba expressions and combinations of local languages to communicate explicit messages on TikTok has also raised questions about potential moderation loopholes.
Speaking to ITWeb Africa, Fırat Mıhcı, a computational linguist and founder of HumanizeMy.ai, says platforms should disclose moderation error rates for individual languages rather than rely on aggregate performance figures that may conceal weaknesses in less-supported languages.
He warns that limited training data can increase the risk of false positives, where legitimate content is removed, and false negatives, where harmful content escapes detection.
Mıhcı also advocates human review of appeals involving underrepresented languages and independently assessed performance data to help researchers and regulators evaluate moderation systems.
Rob Illidge, chief executive of social media analytics firm Vulse, says English-first platform design can shape how people communicate online.
He supports greater transparency around language-specific moderation accuracy, appeal outcomes and human moderator capacity, although he does not have platform-level data measuring such disparities.
Redlam Bs, founder of The Arabic Keyboard, highlights how dialects, transliteration and spelling variations can complicate automated language processing.
In North Africa, for example, Arabic dialects may be written using Arabic script or Latin characters and numerals, creating additional challenges for systems designed to classify text.
ITWeb Africa has previously reported on the challenges of online safety and content moderation across African markets. In March 2025, it reported that TikTok said its content removals across sub-Saharan Africa had increased substantially, with the platform attributing the trend to investment in automated moderation and human safety expertise.
The language-data challenge extends beyond content moderation.
ITWeb Africa has also reported on initiatives to develop AI resources for African languages, including efforts by the Masakhane African Languages Hub to encourage the creation of high-quality, locally relevant datasets.
Such initiatives address a wider technical challenge: AI systems need representative language data to process regional vocabulary, dialects and linguistic context effectively.
A 2025 AfriHate study found differences in hate-speech detection across 15 African languages, with a multilingual model scoring 59.41 for Yoruba and 88.33 for Igbo.
Although these scores do not reflect commercial platforms’ actual moderation accuracy, they highlight the need for language-specific data and independent testing to identify online safety gaps and strengthen accountability.
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