AI Turned My Academic Journal Article Into An Engaging Podcast For Social Media Pros In Minutes with Google’s NotebookLM.

 I recently published academic research in the Quarterly Review of Business Disciplines with Michael Coolsen titled, “Engagement on Twitter: Connecting Consumer Social Media Gratifications and Forms of Interactivity to Brand Goals as Model for Social Media Engagement.” Exciting right?

If you’re a research geek or academic maybe. A social media manager? No way. Yet, I know the findings, specifically our Brand Consumer Goal Model for Social Media Engagement is very exciting for social media pros! So I wanted to write this blog post.

But, as you can tell by the title, an academic audience, and a professional audience are very different. Taking a complicated 25-page academic research article and translating it into a practical and concise professional blog post could take me hours.

I’ve been meaning to experiment with Google’s new AI generator tool NotebookLM so I thought I would try it. Thus, this blog post is about our research on a social media engagement framework and how I used AI to streamline my process to create it. As a bonus, I got a podcast out of it!

My co-author and I did the hard work of the research. I was okay with an AI assistant helping translate it into different media for different audiences. Click for an AI Task Framework.

Using NotebookLM.

Our study was on types of content that generate engagement on Twitter, but the real value was a proposed model for engagement. So before uploading any of the research into the AI tool, I condensed it to just the theoretical and managerial implications sections. Then I added a title, the journal citation, and saved it as a PDF.

NotebookLM uses Gemini 1.5 Pro. Google describes it as a virtual research assistant. Think of it as an AI tool to help you explore and take notes about a source or sources that you upload. Each project you work on is saved in a Notebook that you title. I titled mine “Brand Consumer Goal Model for Social Media Engagement.”

Whatever you upload NotebookLM becomes an expert on that information. It uses your sources to answer your questions or complete your requests. It responds with citations, showing you original quotes from your sources. Google says that your data is not used to train NotebookLM, so sensitive information stays private (I would still double-check before uploading).

Source files accepted include Google Docs, Google Slides, PDF, Text files, Web URLs, Copy-pasted text, YouTube URLs of public videos, and Audio files. Each source can contain up to 500,000 words, or up to 200MB for uploaded files. Each notebook can contain up to 50 sources. If you add that up NotebookLM’s context window is huge compared to other models. ChatGPT 4o’s context window is roughly 96,000 words.

When you upload a source to NotebookLM, it instantly creates an overview that summarizes all sources, pulls out key topics, and suggests questions to ask. It also has a set of standard documents you can create such as an FAQ, Study Guide, Table of Contents, Timeline, or Briefing Doc.

You can also ask it to create something else. I asked it to write a blog post about the findings of our research. You will see that below. Yet, the most impressive feature is the Audio Overview. This generates an audio file of two podcast hosts explaining your source or sources in the Notebook.

The NotebookLM dashboard gives you a variety of options to interact with your sources.

Using Audio Overviews.

There are no options for the Audio Overview so you get what it creates. But what it creates is amazing! My jaw literally dropped when I heard it. And it will give you slightly different results each time you run it.

I noticed things missing in the first audio overview such as the journal and article title and the authors’ names. I did figure out how to make adjustments by modifying my source document. Through five rounds of modifying my source document, I was able to get that information in and more.

Sometimes overviews aren’t 100% accurate. It says, “NotebookLM may still sometimes give inaccurate responses, so you may want to confirm any facts independently.” In our research article we give a hypothetical example of a running shoe brand following our model. It was not real. But in one version of Audio Overviews, the podcast hosts talk as if the company did what we said and got real results that we measured.

I was impressed that in other versions it didn’t use our example and applied the model to new ones. One time it used an organic tea company and another time a sustainable clothing brand. On the fifth attempt it even built in a commercial break for the “podcast.” This last version gave my running shoe example and added its own about a sustainable activewear brand.

What’s really interesting about the last version is that it pulled in other general knowledge about social media strategy and applied it to the new information of our study. At the end, the hosts bring up how our engagement model will help know what to say but that social media managers still need to customize the content to be appropriate for each social platform. That’s a social media best practice but not something we mention in the article.

The Audio Overview Podcast NotebookLM Created.

 

It’s amazing these podcast hosts discussed our research and explained it so well for social pros. What’s more amazing is that they are not real people! Yet NotebookLM did more. Below is the blog post it wrote. It included our diagram of the model, but had trouble getting it right. So, I replaced the image with one I created from our article.

Brand Consumer Goal Model for Social Media Engagement.

This post examines a model for social media engagement based on an October 2024 study in the Quarterly Review of Business Disciplines. “Engagement on Twitter: Connecting Consumer Social Media Gratifications and Forms of Interactivity to Brand Goals as Model for Social Media Engagement,” published by Keith Quesenberry and Mike Coolsen.

The Brand Consumer Goal Model for Social Media Engagement is a framework to help social pros create more effective plans by aligning brand goals with consumer goals. It emphasizes understanding the motivations behind consumer engagement and tailoring content accordingly.

How the Model Works

The model outlines three key brand goals:

  • Building brand community (Reach): This goal focuses on expanding the brand’s audience and increasing awareness.
  • Building brand-consumer relationships (Response): This goal aims to foster brand interaction and engagement.
  • Building brand-consumer bonds (Recognition): This goal seeks to create brand emotional connections and loyalty.

Each brand goal is associated with a corresponding consumer goal and form of social media engagement as seen in the graphic below. Consumers can manage people’s impressions and persuade others by sharing certain brand posts. They can gain information about a brand with other types of brand content for deliberation. Then they can bond with others regulating their emotions in evaluating brand posts with other fans.

Brand Consumer Goal Model for Social Media Engagement Template
Click on the graphic to download this model for social media engagement.

Here is an example

  • A sneaker brand launches a running shoe and aims to build brand community (reach) creating content that appeals to runners’ desire for impression management and persuasion. This could be sharing inspirational stories about runners breaking records encouraging retweets and brand visibility.
  • Once awareness is established, the brand could shift its focus to building relationships (response) by providing information about the shoe’s features and benefits, appealing to consumers’ information acquisition goals, and prompting replies and comments seeking further details.
  • Finally, the brand can foster brand bonds (recognition) by sharing content that resonates with runners’ social bonding and emotion regulation needs, such as posts about the challenges and rewards of training, which encourage likes and emotional connections.

Key Insights from the Study

The study found that simply using popular content types like videos or photos is not enough to guarantee success on social media. The message delivered with the content is crucial.

Marketers need to consider:

  • The target audience’s motivations for using social media
  • Buying cycle stage (awareness, consideration, purchase, loyalty)
  • Desired word-of-mouth function (sharing, deliberation, evaluation)

The Brand Consumer Goal Model for Social Media Engagement offers a strategic framework for developing effective social media campaigns. By understanding the motivations behind consumer behavior and aligning content with both brand and consumer goals, marketers can achieve better results and build stronger relationships with their target audience.

I hope you found this look at NotebookLM and the insights from our social media research helpful. In what ways do you think NotebookLM can help in your job? In what ways can the insights from the Brand Consumer Engagement Model improve your social media content strategy?

NotebookLM Could be a Great Study Tool for Students.

NotebookLM could be a great tool for student learning if used as a study guide, reinforcement, or tutor. It would have a negative impact if used to simply replace reading and listening in the first place. What’s missed when you use AI in the wrong way is depicted in the graphic below. It is from a previous post on the importance of subject matter expertise when using AI

Personally, I was fine using this tool in this way. My co-author and I did the hard work of the research. This AI assistant simply helped us translate it into different media for different audiences.

This graphic shows that in stages of learning you go through attention, encoding, storage, and retrieval. You need your brain to learn this process not just use AI for the process.
Click the image for a downloadable PDF of this graphic.

Half of This Content Was Human Created!

UPDATE: Customize Audio Overviews Before Processing.

Google released a new version of NotebookLX where you can customize the Audio Overview before processing. I was very impressed with this feature. For example, I had another academic article published about a new no tech policy in the classroom that I implemented after COVID restrictions were released.

I uploaded this academic article and before processing I Customized the Audio Overview telling NotebookXL that my target audience was college students distracted by technology in the classroom and to keep the overview shorter for their short attention spans. Here is the result:

 

UPDATE: Interrupt Audio Overview To Ask Questions With Voice.

With the latest release Google has added the ability to engage directly with the AI hosts during an Audio Overview. I’ve tried it and it works creepily well.

I created an Audio Overview of my student professional blogging assignment for personal branding. In the beginning the hosts tell students to write about their unique skills. I clicked a “Join” button and the host said, “Looks like someone wants to talk.” I asked, “How do you know your unique skills?” They said “good question,” gave good tips and continued with the main subject.

Later I interrupted and asked, “Can you summarize what you have covered so far?” They said sure, gave a nice summary and then picked back up where they left off. Finally, I asked about being nervous putting a blog out in public. The hosts reassured me that I don’t have to be perfect. People value honesty and personality. It’s not about perfection.

Generative AI Has Come Quick: What’s Out, What’s Coming, and What to Consider.

A table of Generative AI tool options.

ChatGPT was released to the public six months ago and quickly became the fastest application to reach 100 million users. OpenAI reached this milestone in just two months compared to TikTok’s 9 months and Instagram’s 2 ½ years.

The result of this enormous attention is that the world has quickly become aware of the advanced capabilities of generative AI. As of March 2023, 87% of consumers had heard of AI and 61% somewhat understood what generative AI is and how it works.

ChatGPT generates text from text prompts through a chatbot, but that’s not all generative AI can do. The popularity of ChatGPT also brought attention to OpenAI’s image generation tool. DALL-E 2 generates images from text prompts through a chatbot.

A table listing and describing generative AI integration in major software platforms.
Which generative AI tools will you use for digital and social media marketing?

Despite the mass attention, AI tools have been around for years.

I first wrote about AI in a 2019 post “Artificial Intelligence And Social Media. How AI Can Improve Your Job Not Steal It.” In it, I talked about how AI was being used in algorithms, automation, machine learning, natural language processing, and image recognition.

That post also talked about how AI was used in chatbots to simulate human conversion, in predictive and prescriptive analytics, and in content generation. Examples included Patern89 which has been using AI to analyze content combinations and placement for optimization since 2016. Another example was Clinch which has used AI for content automation and personalized dynamic ad content across channels for years.

Since ChatGPTs release, there’s been a race to integrate generative AI.

The race began with ChatGPT being added to Microsoft’s Bing search engine. Then Google announced plans to integrate its generative AI Bard into Google search. Other platforms quickly announced integrations with OpenAI’s ChatGPT and Google’s Bard such as Salesforce, Hootsuite, HubSpot, and Adobe. Microsoft and Google are even integrating ChaptGPT and Bard into Microsoft 365 and Google Workspace office software for writing, spreadsheets, and slides.

Yet they’re not the only options. Other generative AI tools include Jasper.ai and Copy.ai, for writing, and Midjourney and Stable Diffusion for image generation. Tools like Synthsia generates videos with human avatars and professional voiceovers from text prompts. Other examples of generative AI are summarized below.

  AI content generation tool uses:

  • Content research/Data collection
  • Brainstorming/Idea generation
  • Copywriting/Copyediting
  • Summarizing/Note taking
  • Image (photo/illustration) generation
  • Video clip/Podcast clip generation
  • Transcript generation/Automated post prep
  • Ad/Post variation generation
  • Video generation
  • Podcast/Voice over generation
  • Presentation generation

Generative AI tools come with new skills and considerations.

A new skill with these next gen tools is prompt writing. Prompts are the natural language used to ask a generative AI tool to produce something. More descriptive specific prompts produce better results like prompts that describe the tone of writing or style of an image. Yet be mindful potential of copyright issues with prompts to create text or an image in the style of a famous person without their permission.

A new consideration is the data set from which you train AI. Generative AI tools like ChatGPT are trained on data from the open internet. This is what makes it so powerful, but this is also what can lead to copyright issues and sometimes create bias or incorrect results.

Other AI tools like Jasper.ai allow you to train on a specific dataset. For example, a brand could upload all its previous materials to establish a brand voice to write new copy. Adobe’s Firefly draws from Adobe’s stock library and tracks creator images used to ensure copyright compliance.

With the explosion of AI comes limitations and cautions.

Despite the mass adoption, this technology is in its early stages. There hasn’t been a lot of testing. Regulations, laws, and professional standards have yet to be developed. HubSpot suggests the following limitations, cautions, and warnings in using generative AI tools.

  Cautions when using generative AI:

  • AI can’t conduct original research or analysis.
  • AI can get things wrong so you must fact check.
  • AI doesn’t have lived experience and human insight.
  • AI doesn’t ensure quality, strategy, and nuance.
  • AI can contain biases that are not caught by filters.
  • AI can have plagiarism and copyright issues.

Despite these cautions, alarm over societal harm, and escalating calls for regulation, the AI race is on. Even while companies, government, and scientists raise concerns, companies continue to integrate AI into mainstream products and services. Below is a sample of what’s been released or announced thus far.

Examples of Early AI Content Generation and Automation Tools in Major Platforms.

Platform Tool Function
Hootsuite OwlyWriter AI Generates social media captions from URLs in different tone or voice, content ideas from prompts, auto recreation of top posts, and calendar events copy.
HubSpot Content Assistant Generate copy for blog posts, landing pages, emails and other content from idea to outline and copy generation.
ChatSpot Conversational bot that automates CRM tasks including status updates, managing leads, finding prospects, generating reports, forecasts, and follow-up drafts.
Salesforce Einstein GPT Auto-generates sales, service, and marketing tasks, content, targeting, messaging, reporting and personalization across channels.
Adobe Firefly Generate images, fill, text effects, and recolor from text prompts plus create content, and templates and edit video with simple text prompts – some inside Creative Suite.
Sensei GenAI Automates tasks, optimizes and generates content and content variations across channels in Adobe’s Experience Cloud marketing platform.
Canva Magic Write Generates copy, outlines, lists, captions, ideas, and drafts from text prompts.
AI Image Generator Generates images from text prompts and various styles and aspect ratios.
Meta AI Sandbox Tools that generate multiple versions of text and backgrounds, plus autocropping creative assets for various ad formats on Facebook and Instagram.
Grammarly GrammarlyGo
Generates writing and revisions relevant to tone, clarity, length, and task via text prompts in documents, emails, messages, and social media.
Microsoft Microsoft 365 Copilot Generates tasks, content, documents, presentations, spreadsheets, emails, reports, summaries, updates across Word, Excel, PowerPoint, Outlook and Teams via text prompts and Business Chat.
Google Google Workspace Bard Generate drafts, replies, summaries in Gmail, drafts, summaries, proofs in Docs, images, audio and video in Slides, auto analysis in Sheets, and notes in Meet.

Do Consumers (Your Customers/Target Audience) Want AI?

Another consideration with artificial intelligence is the value consumers may put on human generated content and transparency in the use of AI. I began this article by saying that 87% of consumers are now aware of AI. In fact, 4 in 5 of them are convinced that it is the future.

Yet knowing something is the future and wanting that future are different things. The same consumer survey reveals that 3 in 5 (60%) are concerned or undecided about that future. What people are most concerned about is that AI will change what it means to be human.

As marketing communications professionals we need to stay up to date with all these technology advancements. We should use the latest tools to improve our profession and results for our business or clients. But we should also ensure that new technology is used responsibly and transparently.

Over 77% of consumers say brands should ensure biases and systems of inequality are not propagated by AI-based applications. Over 70% believe brands should disclose when they use AI to develop products, services, experiences, and content.

You Decide How To Best Use AI.

At its best, AI can help with the mundane, repetitive tasks of social media and digital marketing management. At its best, AI will enable you to focus on higher level strategic thinking. At its best, AI will not replace humans, but enable us to be more human.

It’s been 6 months since generative AI was brought to mainstream awareness. Companies are rushing to integrate this technology into everything they do. While we wait for regulations, laws, and professional standards to catch up, let’s use our own judgment in deciding when, where, and how best to use it.

For my latest insights into AI, I began a blog series in Summer 2024 with

Artificial Intelligence Use: A Framework For Determining What Tasks to Outsource To AI [Template]

This Was Human Created Content!