online communication

Generational Differences in Social Media Communication

Giordano Camera, Dylan Carr, Phoebe Haas, Nicole Wasserman

Have you wondered why your dad sends you extremely long texts compared to your best friends, who use memes and slang phrases for most of their communication? In our study, we explored two generations, Generation Z and Generation X and their language use on online social networking sites. We studied different social media posts between the two generations and looked at the differences in how they communicate, especially using text-dominant platforms. We used a plethora of social media sites to validate our findings, but our main areas of study were Facebook and Twitter/X. Our study concluded that Generation Z uses fewer words, more images in their post, and more slang phrases than Generation X does. We want our findings to highlight the contrast between the way these two generations communicate, as miscommunication can lead to unnecessary conflict. Our research contributes to the process of cataloging online communication trends among different generations.

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Introduction

Our group aimed to study the differences in how different generations communicate on various social media platforms. Since its creation, social media has become a place where individuals can communicate with each other in ways they could not have before. People also tend to communicate on a topic that is currently popular in a particular social group, regardless of age (You et al. 2017). Using that factor, we can find valuable data that proves our hypothesis correct. Our research method proved perfectly accurate, as all of our data was correct, with minimal gaps in our study. Our group hopes that the data findings we provided also propel researchers to study differences in communication for other generations. The prevalence of social media is only growing, so our data can act as a stepping stone for future studies.

Methods

Our primary method of data collection revolved around influential people on social media. We would look at social media posts discussing a variety of topics (such as sports, popular culture, and politics) in order to look at data from a wide range of people, like Donald Trump. We made sure to expose ourselves to Generation Z and Xers from each perspective. This is because there are a lot of varying opinions by a diverse group of people on popular posts compared to smaller tweets that may have more of a hive-mind mentality. In addition, we looked at a variety of topics to get stronger evidence and to ensure the communication differences we found were not due to any topic differences (Achinstein, 1994).

Originally, we planned to contact people on Twitter or Facebook to gather their age, but instead, we only targeted accounts where they said their age in a previous post/bio or where it was publicly available (like a celebrity). Once we collected twenty Twitter/Facebook accounts from each generation, we randomly selected three accounts from each generation to really do a deep dive on. While we didn’t interview the people behind the social media accounts that we found like the researchers at Pennsylvania state did, we found that it was unnecessary as all information was available publicly (Zhao and Rosson 2009). In a matched pairs case study, we assembled all of the posts in an easy to view format and then compared the content of each generation’s posts. We noticed a variety of clashing factors across generations, and simultaneously noticed similarities within.

Results

After conducting our research, we found that there were many differences between the ways Generation X and Generation Z communicate online in posts and tweets on social media. Our results showed that differences in online communication was not dependent on the topic of the post or tweet (ie. sports, popular culture, politics) nor the social status of the user (ie. celebrity or common folk), but rather the age generation of the user.

One difference we found in the posts and tweets we analyzed was the number of words that each generation tended to put in their post/tweet. As seen in Chart 1 below, the average number of words on a post/tweet by a member of Generation X was 67 words and the median was 43 words. As seen in Chart 2, the average number of words by a member of Generation Z was 13.4 words, with the median being 11 words. From this, we concluded that Generation X tended to conduct more lengthy and descriptive posts with complete sentences in comparison to the younger Generation Z.

Another difference between the generations we found from our results includes the tendency for Generation Z to incorporate pictures in their posts/tweets and for the lack of imagery in posts/tweets by Generation X. Below is an instance where a member of Generation Z, Bilbo Baggins, uses a picture in their tweet about Trump’s recent conviction and the member of Generation X, Patrick Jones, does not.

Another contrast was the tendency for Generation X to more likely include words in all capital letters and the tendency for Generation Z to have slang terms in their posts/tweets. Below is an instance where a member of Generation Z, bella, uses a slang term, “brain rot” and the member of Generation X, Richard Shepard, does not include any slang words. The term “brain rot” is a slang term used by Generation Z (TikTok “Brain Rot”: How TikTok Is Changing the Way Gen Z Speaks | Redbrick Life&Style, 2024). Also in the example below, the member of Generation X has two words in all capital letters while the member of Generation Z only has one, and it is a shorter word. Both users are discussing the recent “Challengers” movie.

Discussion

Our findings demonstrate clear online communication trends within both generations that are not shared by the other generational group. These distinct patterns in writing and visual communication on social media add to our understanding of how different generations communicate in ways that do not always align with one another. These differences can contribute to intergenerational misinterpretations and tension. Our project identified what some of the prominent generational patterns on social media are, which are beneficial findings that provide a basis for wider intergenerational understanding. Additionally, it lays the groundwork for future research, such as the intricacies of these patterns and how the other generation perceives them.

The results of our research aid in our understanding of two broader phenomena: generational differences and online communication trends. As social media continues to grow and become a staple in people of all ages’ lives, it becomes a new arena for intergenerational tension to arise and unfold. Certain aspects of an age group’s communication can be specific and unique, and does not usually reflect ill intent. This knowledge is important for maintaining dialogues between multiple age groups, so that they do not fall to misunderstandings due to believing a form of speech was rude. For example, Gen X’s use of all capital letters for certain words could potentially be read as aggressive by a younger person who rarely does so, while Gen Z’s use of slang and images may appear unserious or confusing to an older person. Previous research has demonstrated similar phenomena, such as younger people finding the use of periods in text messages to have a negative valence and make the message insincere (Gunraj et al., 2015). Knowing these communication methods are simply an attribute of their generation can ease any potential misgivings on the receiver’s end.

Analyzing the patterns found in our research can also contribute to future literature about online trends and cycles. Gen Z especially uses numerous contemporary references and constantly evolving slang terms and reference images that reflect the state of the internet and popular culture, particularly within their generation’s main bubble on the web. Our findings contribute to the academic understanding of social media trend cycles and communication.

In conclusion, our research begins to catalog numerous generation-specific social media communication patterns into the literature on online communication. We provide many examples of observable differences between how Generation X and Generation Z structure text-based posts on social networking sites, often in ways that directly contrast each other. Though we can offer hypothetical insights into potential misunderstandings these may cause, we recognize that further research is required to analyze these trends in full and begin to study how they verifiably contribute to intergenerational conflict.

References

Achinstein, P. (1994). Stronger Evidence. Philosophy of Science, 61(3), 329–350. https://www.jstor.org/stable/188049?seq=21

Gunraj, D. N., Drumm-Hewitt, A. M., Dashow, E. M., Upadhyay, S. S. N., & Klin, C. M. (2015, November 22). Texting insincerely: The role of the period in text messaging. ScienceDirect. https://www.sciencedirect.com/science/article/abs/pii/S0747563215302181?via%3Dihub

TikTok “Brain Rot”: How TikTok Is Changing The Way Gen Z Speaks | Redbrick Life&Style. (2024, April 22). Redbrick. https://www.redbrick.me/tiktok-brain-rot-how-tiktok-is-changing-the-way-gen-z-speaks/#:~:text=The%20language%20associated%20with%20Generation

You, Q., García-García, D., Paluri, M., Luo, J., & Joo, J. (2017). Cultural Diffusion and Trends in Facebook Photographs. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 347-356. https://doi.org/10.1609/icwsm.v11i1.14902

Zhao, Dejin, and Mary Beth Rosson. (2009). How and why people twitter. Proceedings of the ACM International Conference on Supporting Group Work, https://doi.org/10.1145/1531674.1531710.

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Bridging Bytes and Cultures: The Impact of AI on Linguistic and Cultural Nuances in Online Conversations

Ley’ah Mcclain-Perez and Ivan Pantoja Tinoco

The digital era is marked by the ascension of artificial intelligence. In particular, this presentation will delve into the transformative influence of ChatGPT on the online communication landscape, particularly within the microcosm of X. This AI-driven tool created by OpenAI not only redefines user interactions but also molds the linguistic contours of digital discourse. Our inquiry is rooted in a critical analysis of ChatGPT’s integration into social platforms, assessing its impact on the quality of communication, user perceptions, attitudes, and the ensuing ethical dilemmas.

Our research navigates through the multifaceted ramifications of ChatGPT, exploring its syntactic coherence and semantic relevance, alongside its occasional pitfalls that may lead to misinterpretations. It highlights the diverse demographic engaging on X, using ChatGPT for various purposes ranging from casual interaction to more substantial exchanges, thus painting a broad spectrum of digital human-AI interaction.

This exploration is not merely an academic exercise but a pivotal discourse that contributes to understanding the nuanced dynamics of digital communication in the AI era. It poses critical questions about the future of online interactions, the role of AI in shaping public discourse, and the ethical boundaries of AI integration into social platforms.

Figure 1: Demographics showing the potential of AI in the case of ChatGPT

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Introduction

In the fast-paced world of online communication, the integration of artificial intelligence (AI) technologies has brought a significant shift in how people interact and engage with digital platforms such as emailing or Twitter. At the forefront of this transformation is ChatGPT, an advanced AI language model developed by OpenAI. With its widespread adoption, particularly on platforms like Twitter, ChatGPT has sparked a wave of curiosity and inquiry into its impact on interpersonal communication dynamics.

 ChatGPT’s presence on Twitter is hard to miss. Its ability to generate text responses that closely mimic human speech has made it a staple tool for many users across the platform. From casual conversations to more formal discussions, ChatGPT has seamlessly integrated into the online social media platform Twitter, which can be deceiving for those who have not incorporated AI into their lifestyles.

Twitter, renowned for its role in facilitating global connectivity, serves as a hub for real-time conversations and idea exchange. Users from diverse backgrounds come together on the platform to share thoughts, opinions, and news. With ChatGPT now part of the conversation, the dynamics of communication have undergone a subtle yet significant transformation, prompting researchers to delve deeper into its implications.

Our study seeks to address several key research questions:

  1. How does the integration of ChatGPT influence the quality and nature of communication within social media and discussion forums?
  2. What are the perceptions and attitudes of users toward ChatGPT-generated content in online interactions?
  3. What ethical considerations arise from the use of ChatGPT in facilitating online communication, and how do users navigate these concerns?
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Figure 2: The Inside Story of ChatGPT’s Astonishing Potential | Greg Brockman | TED 

Methods

Our study involved a qualitative analysis of tweets gathered from X users. We collected tweets related to ChatGPT usage from teachers, recruiters, job applicants, and corporate employees. These backgrounds are important to note as these X users were most commonly found tweeting about our topic. Some keywords that we used to find tweets include “ChatGPT email,” “ChatGPT job”, and “ChatGPT ethics.” We filtered each search by tweets from the past year and tweets with the most engagement. Once we obtained these tweets, we analyzed the user’s holistic profile to ensure they were not a bot user. These keywords and filters provided a wide range of perspectives on ChatGPT integration in online communication.

Our analysis focused on categorizing tweets into for and against ChatGPT usage and into categories based on their employment background to discern users’ attitudes towards ChatGPT-generated content. In order to accomplish this, we examined the tone and vernacular of the tweets to distinguish the X user’s attitude. Furthermore, we also examined any memes or emoticons in the user’s tweets as this allowed us to better interpret the tone behind their tweet. Analyzing the tone, vernacular, memes and emoticons was crucial for us to depict any sarcasm.

Results and Analysis

After ChatGPT was implemented in online discourse, our analysis shows notable changes in the tone and linguistic expressions of conversations. The language used is a combination of official and casual, and ChatGPT comments frequently reflect the conversational tone that permeates online interactions. The limitations of AI-generated material in preserving contextual nuances are highlighted by difficulties in interpreting linguistic nuances including comedy, sarcasm, and cultural references.

Discussion

The results of our research provide a more comprehensive view of how online conversation is changing in the era of artificial intelligence. Although ChatGPT makes communication easier and improves accessibility, its effects on language use and cultural dynamics need to be carefully considered. Through adept handling of AI-generated content, we can optimize its potential to enhance digital connections while reducing the likelihood of misunderstandings and cultural insensitivity.

The key to maximizing AI-generated content’s ability to promote meaningful conversation while avoiding communicative dangers is to manage it strategically. Through the development of cultural sensitivity and contextual cue awareness, users may skillfully negotiate the complex landscape of online communication, utilizing AI’s augmentative powers without sacrificing human connection.

An ongoing conversation about the development of digital literacy and appropriate AI use is essential to this project. Giving consumers the means to understand language nuances and make sense of subtle implications helps guarantee that the emergence of AI-driven communication will continue to be a positive force for change.

References

Carrie Bradshaw Hater. “My Students Are Using CHATGPT for Their Essays and Everyone Is Turning in the Same Essay!!!” Twitter, Twitter, 4 Mar. 2024, twitter.com/nancytaughtyou/status /1764491684446900521.

Courtney Glory to Heroes Wells. “I Used CHATGPT When I Was Overtired and Needed to Get an Email to My Daughter’s Teacher That Made Sense and Didn’t Trust My Own Editing. I Don’t Care If She Knows — It’s Better than the Soup She Would Have Gotten If I Did It on My Own.” Twitter, Twitter, 28 Feb. 2024, twitter.com/ndesquiress/status/1762909572807708894.

Kepha, Brian. “Maaan ,CHATGPT Is Great at Corporate Lingo and I Love This.with It, It Is so Easy to Communicate a Serious and Business Tone Especially on Emails and Job Applications. This Is a CHATGPT Appreciation Tweet.” Twitter, Twitter, 29 Feb. 2024, twitter.com/AngelofVerdant/status/1763065674501394463.

Gawne & McCulloch. “Emoji as Digital Gestures.” Language@Internet, 2019.

Posts, Hannah. “It’s Hard to Tell. English Isn’t Her First Language, but the Communication Program I’m Using (I Lied, It’s Not Actually Email) Has a Translate Function She Could Use. in This Particular Instance She Was Just Replying, so All She Needed to Say Was ‘OK.’” Twitter, Twitter, 28 Feb. 2024, twitter.com/HannahPosted/status/1762946495324500456.

Sharma, S., & Yadav, R. (2023). Chat GPT – A Technological Remedy or Challenge for Education System. Global Journal of Enterprise Information System, 14(4), 46-51. Retrieved from https://www.gjeis.com/index.php/GJEIS/article/view/698

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