Editor’s note: we are republishing one of the emails from The Fix’s new AI newsletter course that offers perspective and practical advice on artificial intelligence for news leaders by Alberto Puliafito. You can subscribe for free to access the whole course.

Social media is not just a platform for distributing content; it’s a space for engaging with audiences, monitoring trends, and even breaking news. AI tools have become indispensable in managing these tasks more efficiently and effectively.

In this article we’ll dive into the various ways AI can streamline social media management, enhance audience engagement, and analyse trends. Also, we’ll see a very practical suggestion to use AI to create a proper draft for your social media.

The importance of social media in journalism

Social media has transformed journalism, offering new ways to reach and interact with audiences. It’s a powerful tool for content distribution, audience engagement, and real-time news dissemination. However, managing multiple social media platforms, monitoring trends, and engaging with audiences can be overwhelming. This is where AI comes in — offering tools to automate, optimise, and analyse social media activities, freeing journalists to focus on content creation and strategic engagement.

But first of all, let’s see a very practical technique for draft content creation. One of the biggest problems with AI chatbots is that if you ask for a social post you will often receive as an output something that sounds naive or kitsch or that simply does not resonate with your style.

A possible solution? Teach the AI how you want your own posts!

In this example – very quick. You can be more accurate – I gave ChatGPT twenty screenshots of my posts on LinkedIn. I asked it to

analyse structure, tone of voice, and style of my posts;

wrote guidelines based on the analysis
prepare three draft posts for my Linkedin profile, telling the story of the course you are following

You can expand this technique, using more examples, building personal assistants (i.e. the personalised GPTs for ChatGPT, or the agent in Claude) and using the technique for other pieces of content.

Let’s see what ChatGPT did. First of all, it provided the requested analysis

Then, it wrote the guidelines.

Finally, it created the drafts.

This is not me, yet. But it’s definitely better than what ChatGPT does without this technique: these are three acceptable drafts I’ll work on. Once I’ll be happy with the result, I’ll provide ChatGPT the final version, asking to learn and update guidelines on my editing basis.

[contenpost url=https://thefix.media/2023/1/20/how-linkedin-became-the-next-best-option-for-journalists-and-media-leaders-after-musk-took-over-twitter]

Other functions

AI tools can also automate the scheduling and posting of content across various social media platforms. These tools can optimise post timing based on historical data, finding when your audience is most active, ensuring maximum reach and engagement.

They can also automatically adjust posts for different platforms to maximise impact. AI can monitor social media in real-time, identifying trending topics, hashtags, and conversations that are relevant to your beat. This allows journalists to stay ahead of the curve and engage with emerging stories as they unfold.

AI can help personalise interactions with your audience by analysing their behaviour and preferences. This enables more targeted content and personalised responses, fostering deeper engagement. AI allows to segment your audience and tailor content specifically to different groups, ensuring that your social media strategy is aligned with audience interests.

Tools like HubSpot or Salesforce’s Social Studio, Hootsuite or Later Social or Brandwatch may be the case for newsrooms: they need to be tested and they need experiments. Here you can find an up-to-date list of social tools.

You can also use generative AIs for social media insights. However, it is important to proceed with these activities only after careful consideration and collective agreement within the team, the publisher, and the newsroom. Everyone involved should understand the implications of using AI to analyse and segment audience data. Transparency and consent are key when dealing with audience information, and it’s important to align on how this data will be used, ensuring compliance with privacy policies and ethical standards. Once this is on the right track, you can export:

engagement metrics (likes, shares, comments) from platforms in CSV or Excel formats and upload the data
website analytics: provide data from tools like Google Analytics, including metrics like page views, bounce rates, and time spent on site, to help the AI understand user behaviour.
audience information: provide insights (anonymously) about your audience (genre, age, preferences…)

Finally, you can use prompts like this one:

“Based on the attached social media engagement data (CSV file), identify key audience segments and suggest personalised content strategies for each group.”

AI can analyse the sentiment of social media conversations, helping you understand public perception of your content or brand. This is particularly useful for managing potential PR crises by identifying negative sentiment early and allowing for timely interventions. Again, using AI for sentiment analysis requires agreement within your team or newsroom. It’s essential to consider how this data will be processed and the potential impact of the results. Transparency and ethical considerations should guide the decision-making process.

To run sentiment analysis effectively, you will need to provide the AI with relevant dataset: collect data on comments, tweets, or posts mentioning your brand or content, either via direct API integration or by uploading CSV/JSON files with the relevant data; provide text-based feedback from surveys or customer reviews in a structured format (CSV or text files).

With this data, you can ask the AI:

“Analyse the sentiment of the comments in the attached CSV file from our latest post. Identify any emerging negative trends and suggest potential responses to mitigate PR risks.”

You can also use generative AIs for illustrations. If your newsroom decides to illustrate certain articles with AI-generated images (in line with your internal policy), AI can help create a unique and recognisable style. You can use prompts such as:

“Create an illustration for an article about [insert topic], following a [specific style or aesthetic], ensuring the image is distinctive and aligns with the overall visual identity of our newsroom.”

Once you’ve generated images in a style that suits your brand, you can save these images as references for future projects, ensuring consistency across your visual content. AI tools can then use these references to generate new images in the same style. For example:

“Using this image as a reference [add reference image], generate a new illustration for our upcoming article on [insert topic], ensuring it follows the same style and visual identity.”

We won’t go deeper in this topic, but it’s important to recommend: if you decide to create synthetic content, be careful and transparent, and do not create fake hyper-realistic images.

Ethical considerations in AI-driven social media management

While AI can help manage social media more efficiently, it’s important to maintain transparency and authenticity in your interactions. Audiences value genuine engagement, and overly automated responses can undermine trust.

Use AI to enhance, not replace, human interaction. Ensure that automated responses are personalised and reviewed by a human whenever possible, especially in sensitive situations.

AI can take over many routine tasks, but it’s essential to have human oversight to ensure that content and interactions align with your brand’s voice and values. Over-reliance on AI can lead to a lack of nuance in your social media presence. Set clear guidelines for AI-generated content and responses, and regularly review them to ensure they meet your standards. Human editors should always have the final say on important posts or interactions.

AI-driven social media tools often rely on analysing user data to personalise content and engagement. It’s important to handle this data ethically, respecting user privacy and complying with regulations like GDPR.

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