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Music can be an incredibly powerful tool, and in an age where technology allows us to connect with those that don’t live near us, finding ways to use music to encourage that bonding can be incredibly beneficial. Particularly, in a global pandemic such as COVID-19, finding ways to connect with others remotely is more important than ever. In this study, I will be looking at how one artist in particular, Tessa Violet, manages to continue to not only grow her community, but also encourage her community to bond with each other. She achieves this by finding various ways, unique to her and her branding, that allows her community to connect with each other. By using her platform to give scavenger hunts to her fanbase, promoting and fostering community growth through various platforms, and livestreaming in ways unique to her style, she is able to connect her audience with each other. By engaging and observing these various actions on her part, I am able to see and experience this community bonding myself. I also look at ways this might be able to expanded to other artists and their communities as well. The hope is that the internet and social media can be just another form of music encouraging people to connect with one another.
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In a COVID-19 world, student engagement has suffered drastically as organizations and universities shifted to an online format. Yet, there is still an opportunity and a space for digital content creation to bridge the gap in a virtual and hybrid university lifestyle. This project looks at how student groups can still engage students at ASU Tempe through digital content creation and which tools to use to enter the space.
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Neoliberal feminism has gained significant popularity in fourth-wave feminist media. In this paper, I analyze the 2017 limited television series "Big Little Lies" to uncover the intricacies of neoliberal feminist theory in practice, particularly how it speaks to gender, race, and class relations.
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From our research, we found that for as little as $5 a day, an independent artist can make effective introductions to audiences most likely to enjoy what they have to offer without compromising artistic expression, while also learning from and engaging with their growing audience.
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The paper is meant to serve as a base of reference material for future, real world, project proposals to historical organizations. A mock proposal to the Fullersburg Historic Foundation, proposing the digital mediums and methods discussed, is included at the end of the paper.
In concluding thoughts, Matt I appears to have the depth and breadth of skills needed to make impactful media. And the mediums of media have been as diverse as the subjects they cover. But upon reviewing his own review, it took someone else smarter had to tell me that though there is diversity abound in his portfolio, there is a single baseline: Keeping history alive. Every one of these mediums can be used to infuse a sense of digital energy, simulated life, back into historical artifacts and documentation. The historical characters return to live when a medium brings back the motion, the energy, of the thing it is showing. If the medium infuses this life back into the story. The results are nothing short of magic.
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algorithms for recommending new music to users. However, at the
core of their recommendations is the collaborative filtering algorithm,
which recommends music based on what other people with similar
tastes have listened to [1]. While this can produce highly relevant
content recommendations, it tends to promote only popular content
[2]. The popularity bias inherent in collaborative-filtering based
systems can overlook music that fits a user’s taste, simply because
nobody else is listening to it. One possible solution to this problem is
to recommend music based on features of the music itself, and
recommend songs which have similar features. Here, a method for
extracting high-level features representing the mood of a song is
presented, with the aim of tailoring music recommendations to an
individual's mood, and providing music recommendations with
diversity in popularity.
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