AI, Communication, and Information Exposure
Topics
AI, Communication, and Information Exposure
- How AI Shapes Information Exposure
- Social Media Recommendation and Personalization
- Automated Content Moderation
- Misinformation and Disinformation Detection
- Deepfakes and Synthetic Media
- Generative AI in Scholarly Publishing
- Negative Impacts and Risks
Weekly Discussion: Tracing an AI-Generated Piece of Information
Please submit all graded work via Canvas. Participation requirements and grading details are provided in Canvas.
AI-generated content is increasingly present in everyday communication. Images, videos, music, text, and audio produced or assisted by AI systems now circulate widely across social media platforms, search engines, news feeds, and entertainment services.
This discussion asks you to move beyond general opinions about “AI content” and closely examine one concrete piece of AI-generated information, focusing on how it is identified, interpreted, and circulated within a specific platform context.
Your goal is to analyze how AI-generated information becomes visible, contested, or trusted in practice.
Step 1: Choose a concrete example
Select one specific piece of AI-generated information, such as:
- An AI-generated image or video
- An AI-generated song, voice clip, or audio
- A text post, article, or message created or strongly assisted by AI
- A piece of content suspected to be AI-generated but not clearly labeled
You may base on:
- Something you personally encountered online
- A widely discussed or controversial example
- A case mentioned in the readings or class materials
Briefly describe:
- What the content is
- Which platform it appeared on
- How you encountered it (e.g., recommendation, search, sharing)
Step 2: Platform identification and labeling
Analyze how the platform handled this content.
You may consider:
- Whether the platform explicitly labeled it as AI-generated
- Whether any warnings, disclosures, or context panels were provided
- If no label was present, what reasons might explain its absence
Focus on what the platform made visible or invisible, rather than whether the label was “correct.”
Step 3: Controversy, misinformation, and fact-checking
Discuss whether this piece of information raised concerns.
You may address:
- Whether it sparked controversy, confusion, or debate
- Whether it was challenged as misinformation or subjected to fact-checking
- Whether it could reasonably mislead audiences, even if no correction occurred
Distinguish between intentional deception and potential for misunderstanding.
Step 4: Circulation and audience
Reflect on how the content spread and who it reached.
Consider:
- Whether it was widely shared or remained within a niche audience
- What kinds of users seemed most engaged with it
- How AI-driven recommendation or visibility mechanisms may have shaped its reach
Expected outcome
Your post should present a short, coherent analysis of one AI-generated piece of information by tracing how it was identified, interpreted, and circulated on a platform. A strong post typically:
- Clearly describes a specific example
- Explains how platform design shaped visibility and interpretation
- Reflects on the relationship between AI generation, user trust, and information risk
Reading
- Algorithmic people-pleasers: Are AI chatbots telling you what you want to hear?
- Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking. You can also check the authors' presentation:
- Using AI to detect COVID-19 misinformation and exploitative content, also see IEEE's report
- Social media and the age of AI misinformation | Aishwarya Reganti | TEDxJacksonville
- AI news videos blur line between real and fake reports
- Is Sienna Rose AI-generated? New music artist divides listeners
-
Seeing is no longer believing: Artificial Intelligence’s impact on photojournalism
-
Deepfakes Aren't the Disinformation Threat They're Made Out to Be
- GPTZero finds 100 new hallucinations in NeurIPS 2025 accepted papers
- As Springer Nature journal clears AI papers, one university’s retractions rise drastically
- Researchers who are using generative AI to write scientific papers are publishing a significantly higher number of studies
- More than half of researchers now use AI for peer review — often against guidance
- How AI is transforming research: More papers, less quality, and a strained review system
- AAAI Launches AI-Powered Peer Review Assessment System
- 1 in 5 high schoolers has had a romantic AI relationship or knows someone who has
- Why AI companions and young people can make for a dangerous mix
- Snap settles social media addiction lawsuit ahead of trial
- Ads Are Coming to ChatGPT. Here’s How They’ll Work