AI in social media: use cases, top tools & adoption challenges

Unlike traditional search engines matching entered keywords with a certain profile, channel, or post, AI-powered systems can interpret incorrect or incomplete user input, such as misspelled names or concepts related to the searched content based on contextual parameters. These can include common synonyms, search trends, or user browsing habits, similar to Google Search's autocomplete feature.

While ML’s pattern recognition and user segmentation capabilities certainly help optimize organic content delivery, they’re also used by marketing and sales specialists to interpret market trends and improve advertising management. Social media intelligence systems can analyze written content via natural language processing to monitor trending topics or assess user sentiment, namely the public’s perception of certain brands or subjects. They can also be used to fine-tune social media marketing campaigns to enable targeted advertising based on user interests and behavior.

Given the growing role of influencers as market trend drivers and sales catalysts, marketers need to identify the most suitable content creators and build fruitful collaborations. Modern social media management tools often include influencer marketing features to identify, typically via ML-based analytics, potential candidates who align with specific brands’ values and can promote their products effectively. Parameters to consider include, for instance, influencers’ expertise, post reach, and user base demographics.

The growing importance of social media platforms in digital marketing and sales has also increased the number of customer interactions taking place in their instant messaging applications. To handle this growing interaction volume, various platforms have implemented AI-powered bots with advanced NLP capabilities that allow businesses to communicate with customers around the clock.

Social media platforms are massive pools of visual data. Dedicated AI-based computer vision systems can sift through millions of images and videos on social media to extract valuable insights. For instance, social media marketing-oriented data analytics and BI platforms use computer vision for logo detection to monitor brand mentions and awareness. The same technology can be applied to contextual shopping, which enables users to purchase products appearing in multimedia content directly from the social media platform. Furthermore, AI can automate photo and video tagging for easier content discovery.

Capable of creating complex multimedia content from simple textual instructions (so-called prompts), generative AI has become a staple tool of most social media specialists. Solutions such as GPT, DALL-E, and other AI models enable content creators and media professionals to generate posts, comments, or articles and publish them on multiple platforms faster than ever to enhance social media engagement.

Maintaining an effective and consistent social media strategy across multiple channels is a challenging and labor-intensive task. AI-assisted social media schedulers can help by automating content publishing and reposting while recommending the most suitable publication timing, hashtags, and links depending on your target audience.

AI-powered moderation systems complement human fact-checkers, professional moderators, and reports by users to detect and filter out harmful content more effectively. These tools can scan visual information via computer vision to identify graphic images or monitor comments and posts with NLP to spot hate speech, fake news, spam, and cyberbullying.

Social media users could interact with an increasingly diverse audience and access a growing pool of content if not for language barriers. Many social media platforms have addressed this issue by implementing NLP-based machine translation features to deliver content in multiple languages.