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For this creative project, the student's overarching objective was to establish a signature dish and to learn how to cook. She acquired a cookbook fit for novice cooks, "Eat What You Love" by Marlene Koch, which focused on healthy eating through the reduction of sugar and fat. The student completed thirty recipes including two appetizers, five breakfast entrees, five lunch entrees, twelve dinner entrees, and six desserts. Her culinary ventures were then detailed through a blog site the student had created. Blog posts included a brief description primarily of the portion size, a nutritional analysis of the recipe, enjoyable aspects of the dish, whether something went wrong, what was learned from creating the dish, as well as a photograph of the prepared dish. A large element of this project focused upon food photography and obtaining images that made the created dish look appealing. It was found that the best images were taken in natural lighting with good compositions and pops of color. In order to gain readership, the student developed an Instagram account where she would post images of the food and provide links to her blog entries and recipes. Through this means, she was able to obtain over 100 followers to her blog. In addition to learning how to cook, the student sought out to understand components of a healthy diet and how each nutrient contributes to an individual health. This objective is detailed throughout the course of the paper as well as several other objectives. The student also studied how social media has impacted the way in which we share food and our knowledge of food. Additionally in this paper, the student evaluated the evolution of the USDA Dietary Guidelines and their effectiveness over time. From this project, the student walked away with new knowledge about nutritional eating and lifestyle habits that she will retain for years to come. She hopes that this project will encourage other students to take on their own culinary adventure.
ContributorsMarley, Allison Marie (Author) / Levinson, Simin (Thesis director) / Martinelli, Sarah (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Background: As the growth of social media platforms continues, the use of the constantly increasing amount of freely available, user-generated data they receive becomes of great importance. One apparent use of this content is public health surveillance; such as for increasing understanding of substance abuse. In this study, Facebook was used to monitor nicotine addiction through the public support groups users can join to aid their quitting process. Objective: The main objective of this project was to gain a better understanding of the mechanisms of nicotine addiction online and provide content analysis of Facebook posts obtained from "quit smoking" support groups. Methods: Using the Facebook Application Programming Interface (API) for Python, a sample of 9,970 posts were collected in October 2015. Information regarding the user's name and the number of likes and comments they received on their post were also included. The posts crawled were then manually classified by one annotator into one of three categories: positive, negative, and neutral. Where positive posts are those that describe current quits, negative posts are those that discuss relapsing, and neutral posts are those that were not be used to train the classifiers, which include posts where users have yet to attempt a quit, ads, random questions, etc. For this project, the performance of two machine learning algorithms on a corpus of manually labeled Facebook posts were compared. The classification goal was to test the plausibility of creating a natural language processing machine learning classifier which could be used to distinguish between relapse (labeled negative) and quitting success (labeled positive) posts from a set of smoking related posts. Results: From the corpus of 9,970 posts that were manually labeled: 6,254 (62.7%) were labeled positive, 1,249 (12.5%) were labeled negative, and 2467 (24.8%) were labeled neutral. Since the posts labeled neutral are those which are irrelevant to the classification task, 7,503 posts were used to train the classifiers: 83.4% positive and 16.6% negative. The SVM classifier was 84.1% accurate and 84.1% precise, had a recall of 1, and an F-score of 0.914. The MNB classifier was 82.8% accurate and 82.8% precise, had a recall of 1, and an F-score of 0.906. Conclusions: From the Facebook surveillance results, a small peak is given into the behavior of those looking to quit smoking. Ultimately, what makes Facebook a great tool for public health surveillance is that it has an extremely large and diverse user base with information that is easily obtainable. This, and the fact that so many people are actually willing to use Facebook support groups to aid their quitting processes demonstrates that it can be used to learn a lot about quitting and smoking behavior.
ContributorsMolina, Daniel Antonio (Author) / Li, Baoxin (Thesis director) / Tian, Qiongjie (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The role of technology in shaping modern society has become increasingly important in the context of current democratic politics, especially when examined through the lens of social media. Twitter is a prominent social media platform used as a political medium, contributing to political movements such as #OccupyWallStreet, #MeToo, and #BlackLivesMatter. Using the #BlackLivesMatter movement as an illustrative case to establish patterns in Twitter usage, this thesis aims to answer the question “to what extent is Twitter an accurate representation of “real life” in terms of performative activism and user engagement?” The discussion of Twitter is contextualized by research on Twitter’s use in politics, both as a mobilizing force and potential to divide and mislead. Using intervals of time between 2014 – 2020, Twitter data containing #BlackLivesMatter is collected and analyzed. The discussion of findings centers around the role of performative activism in social mobilization on twitter. The analysis shows patterns in the data that indicates performative activism can skew the real picture of civic engagement, which can impact the way in which public opinion affects future public policy and mobilization.
ContributorsTutelman, Laura (Author) / Voorhees, Matthew (Thesis director) / Kawski, Matthias (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Misinformation, defined as incorrect or misleading information, has been around since the beginning of time. However, the rise of technology and widespread use of social media has allowed misinformation to evolve and gain more traction. This study aims to examine health and political misinformation within the contexts of the COVID-19 pandemic and the 2020 U.S. Presidential Election. Utilizing samples of misinformation from the 45th president of the United States, I analyzed the levels of engagement that this misinformation received on the social media platform X, formerly known as Twitter. I also examined how various Google search query trends changed over time in response to this misinformation. Then, I categorized the data into misleading statistics, misrepresentations of opinions as facts, or completely false content. Lastly, I looked into the physical responses that resulted from the spread of such misinformation. My findings of this case study showed that misinformation received significantly more attention than other social media posts, as evidenced by increased Google searches related to the topics and higher levels of likes and retweets on misinformative Tweets during the specified periods. Furthermore, the former president employed all three types of misinformation, with misleading statistics most prevalent in the health misinformation sample and misrepresentations of opinions as facts most prevalent in the political misinformation sample. The repercussions of this misinformation encompassed individuals ingesting unsafe products, decreased trust in the electoral process, and a violent insurrection at the U.S. Capitol. Despite the existing research in this field, there remains much more to be uncovered regarding the vast amount of misinformation circulating on the Internet.
ContributorsShah, Sona (Author) / Boghrati, Reihane (Thesis director) / Simeone, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor)
Created2023-12
ContributorsShah, Sona (Author) / Boghrati, Reihane (Thesis director) / Simeone, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor)
Created2023-12
ContributorsShah, Sona (Author) / Boghrati, Reihane (Thesis director) / Simeone, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor)
Created2023-12