More Like this

1. User vs. Engineer: Student Perceptions of Responsibility in Social Media

   Stepback, L.; Hwang, M. H.; Claussen, S.; Leonardo, Y. (June 2023, ASEE Annual Conference & Exposition)

   Engineers are responsible to many stakeholders, including the public and their employer. One such responsibility is considering and accounting for the potential impacts and risks associated with a technology that they create. A relatively new, and potentially risky, technology that has been on the rise over the past two decades is social media. The advent of social media, such as Facebook and Twitter, and its integration into our daily lives raises questions about the duty engineers bear for its responsible usage and design versus the responsibilities users have as they use the technology. This paper analyzes qualitative interview data from a study on engineering students’ perceptions of engineering ethics and social responsibility to answer the following research question: In what ways do students change (or not change) how they talk about engineers’ social and professional responsibilities to the technologies they create when framed in the context of social media? Our findings show that mentioning social media as a specific application of engineering ethics rendered visible the relationship between engineers, users, and technology that students then utilized to address the broader question about engineers’ responsibility to the technologies they create. In this study, a total of 33 students from three U.S. universities were interviewed longitudinally, once in the first year of their degree and again in the fourth year. In the interviews, the students were asked about their views on the social and professional duties engineers have for the technologies they create, framed in the context of social media. Analysis of student responses involved open and axial coding of relevant interview portions performed by two researchers to identify common themes and longitudinal changes between student interviews. These themes included: communication between the engineer and user, collective responsibility, benefits to society, high quality engineering, and misinformation. While students typically maintained elements of their views across both interviews, it was also common to see students change their responses to include new themes or exclude themes present in their initial interview. The students tended to believe that engineers have a responsibility to think through potential uses (or misuses) of their technology, but also believe that the users share some responsibility to use the technology appropriately. When social media was mentioned specifically, some students believed that the users were entirely responsible for how the technology is used, occasionally contradicting their views of engineering ethics when probed without the context of social media. This paper highlights the central tension between user responsibility and engineer responsibility. By illuminating students’ views, it will support educators in opening a dialogue with their students about who is ultimately responsible for the design and use of new technologies. 

   more » « less
2. Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy

   Islam, Tunazzina; Goldwasser, Dan (May 2025, Association for Computational Linguistics)

   The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On the other hand, traditional unsupervised methods for extracting themes from public discourse, such as topic modeling, often reveal overarching patterns that might not capture specific nuances. Consequently, a significant portion of research into social media discourse still depends on labor-intensive manual coding techniques and a human-in-the-loop approach, which are both time-consuming and costly. In this work, we study the problem of discovering arguments associated with a specific theme. We propose a generic **LLMs-in-the-Loop** strategy that leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging. To demonstrate our approach, we apply our framework to contentious topics. We use two publicly available datasets: (1) the climate campaigns dataset of 14k Facebook ads with 25 themes and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads with 14 themes. Additionally, we design a downstream task as stance prediction by leveraging talking points in climate debates. Furthermore, we analyze demographic targeting and the adaptation of messaging based on real-world events. 

   more » « less
3. Teens on Tech: Using an Asynchronous Remote Community to Explore Adolescents' Online Safety Perspectives

   https://doi.org/10.1145/3584931.3606967  

   Jean Baptiste, Naulsberry; Park, Jinkyung; Chatlani, Neeraj; Ali, Naima Samreen; Wisniewski, Pamela J. (October 2023, ACM)

   As part of a Youth Advisory Board of teens (YAB), a longitudinal and interactive program to engage with teens for adolescent online safety research, we used an Asynchronous Remote Community (ARC) method with seven teens to explore their social media usage and perspectives on privacy on social media. There was a spectrum of privacy levels in our teen participants’ preferred social media platforms and preferences varied depending on their user goals such as content viewing and socializing. They recognized privacy risks they could encounter on social media, hence, actively used privacy features afforded by platforms to stay safe while meeting their goals. In addition, our teen participants designed solutions that can aid users to exercise more granular control over determining what information on their accounts is to be shared with which groups of users. Our findings highlight the need to ensure researchers and social media developers work with teens to provide teen-centric solutions for safer experiences on social media. 

   more » « less
4. Fake Tweets Detection and Its Impacts on the 2020 U.S. Election Prediction

   Qorib, Miftahul; Kim, Junwhan (October 2024, IEEE)

   The increased social media usage in modern history instigates data collection from various users with different backgrounds. Mass media has been a rich source of information and might be utilized for countless purposes, from business and personal to political determination. Because more people tend to express their opinions through social media platforms, researchers are excited to collect data and use it as a free survey tool on what the public ponders about a particular issue. Because of the detrimental effect of news on social networks, many irresponsible users generate and promote fake news to influence public belief on a specific issue. The U.S. presidential election has been a significant and popular event, so both parties invest and extend their efforts to pursue and win the general election. Undoubtedly, spreading and promoting fake news through social media is one of the ways negligent individuals or groups sway societies toward their goals. This project examined the impact of removing fake tweets to predict the electoral outcomes during the 2020 general election. Eliminating mock tweets has improved the correctness of model prediction from 74.51 percent to 86.27 percent with the electoral outcomes of the election. Finally, we compared classification model performances with the highest model accuracy of 99.74634 percent, precision of 99.99881 percent, recall of 99.49430 percent, and an F1 score of 99.74592 percent. The study concludes that removing fake tweets improves the correctness of the model with the electoral outcomes of the U.S. election. 

   more » « less
5. Behavioral differences: insights, explanations and comparisons of French and US Twitter usage during elections

   https://doi.org/10.1007/s13278-019-0611-9  

   Ian Davidson, Antoine Gourru (January 2020, Social network analysis and mining)

   Social networks and social media have played a key role for observing and influencing how the political landscape takes shape and dynamically shifts. It is especially true in events such as national elections as indicated by earlier studies with Facebook (Williams and Gulati, in: Proceedings of the annual meeting of the American Political Science Association, 2009) and Twitter (Larsson and Moe in New Med Soc 14(5):729–747, 2012). Not surprisingly in an attempt to better understand and simplify these networks, community discovery methods have been used, such as the Louvain method (Blondel et al. in J Stat Mechanics Theory Exp 2008(10):P10008, 2008) to understand elections (Gaumont et al. in PLoS ONE 13(9):e0201879, 2018). However, most community-based studies first simplify the complex Twitter data into a single network based on (for example) follower, retweet or friendship properties. This requires ignoring some information or combining many types of information into a graph, which can mask many insights. In this paper, we explore Twitter data as a time-stamped vertex- labeled graph. The graph structure can be given by a structural relation between the users such as retweet, friendship or fol- lower relation, whilst the behavior of the individual is given by their posting behavior which is modeled as a time-evolving vertex labels. We explore leveraging existing community discovery methods to find communities using just the structural data and then describe these communities using behavioral data. We explore two complimentary directions: (1) creating a taxonomy of hashtags based on their community usage and (2) efficiently describing the communities expanding our recently published work. We have created two datasets, one each for the French and US elections from which we compare and contrast insights on the usage of hashtags. 

   more » « less