My main takeaway from this course is the understanding I have gained regarding sharing media narratives, and especially the implications of publishing in the new age. I learned this mostly from the journey of creating a digital representation and the research I conducted for my critical analysis. It taught me how the simple act of taking and sharing a selfie is much more than capturing a good day; it is about hoping to connect to an audience through an image while communicating a certain message. I’ve not digitally generated a selfie before this class, but after looking at my social media platforms, I can see that there is a certain identity I’ve constructed through the images I’ve chosen to share and captions I pair with them.
Completing the mini-assignment on ephemeral narratives also played a large role. I analyzed an Instagram story that I published and reflected on the choices I made while constructing the post. Barnea et al.’s insights clarified why users are more likely to simply like a story rather than comment because of its fleeting nature. It also made me look inward, finding that I often prefer the highly ephemeral forms of communication which makes conversation more casual with less pressure of crafting a perfect response.
Creating the digital selfie allowed me to try my hand at generative AI. I generally avoided AI, so I had little experience beyond ChatGPT and Google’s AI overview. It was an opportunity to see how a language model would understand my requests and how I might alter them in order to achieve my desired result. The lack of nuance DALL-E had led me to further research how models are trained, allowing me to understand categorization and the bias encoded in these tools. Overall, I come away better understanding why a generated image looks the way it does and having de-mystified the AI model. Rather than avoiding AI ignorantly, I understand that it is a tool that isn’t necessarily supporting me how I need it.
Creating the digital selfie allowed me to try my hand at generative AI. I generally avoided AI, so I had little experience beyond ChatGPT and Google’s AI overview. It was an opportunity to see how a language model would understand my requests and how I might alter them in order to achieve my desired result. The lack of nuance DALL-E had led me to further research how models are trained, allowing me to understand categorization and the bias encoded in these tools. Overall, I come away better understanding why a generated image looks the way it does and having de-mystified the AI model. Rather than avoiding AI ignorantly, I understand that it is a tool that isn’t necessarily supporting me how I need it.
We began to finish off the course with readings on Indigenous storytelling which I found very interesting. Tekobbe offered a well-rounded perspective on the importance of storytelling as it is a means to not only entertain, but to pass on values, knowledge, and culture. While Tekobbe’s article is culturally situated, I found it to be beneficial in understanding how storytelling through social media often occurs. It is factors such as the fluidity of stories and the collaborative nature of knowledge-making that helps to understand how popular content becomes vital to shaping digital culture.
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