Tag: CMC

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.

Analyzing Miscommunication and Preferences in Face-to-Face vs. Texting Among College Students

Adam Bouaricha, Emily Haddad, Ryan Kimura, Usuhe Maston, Natalia Adomaitis

Reportedly, 97% of young adults aged 18 to 24 are actively engaged in texting (Smith, 2011). Central to our inquiry is exploring how college students adeptly navigate misunderstandings and mend communication breakdowns within their text-based interactions with peers, friends, and romantic partners. Specifically focusing on the demographic of college students aged 18 to 22, our study delves into the myriad factors contributing to miscommunication within this cohort. Using a comprehensive mixed-method approach, we integrate surveys with picture-based evidence for enhanced analysis. Drawing upon the framework of multimodal conversational analysis, our research endeavors to unravel the intricacies of repair mechanisms, encompassing trouble sources, repair initiation, and ensuing solutions in text-based interactions. Analysis of our diverse sample of college students unveils that critical trouble sources, such as the absence of tone and social cues, substantially influence the occurrence of misunderstandings. Participants demonstrate a keen awareness of communication breakdowns, prompting proactive engagement in repair solutions to rectify discrepancies. Through rigorous thematic analysis of survey responses, we discern prevalent patterns and adaptive strategies individuals employ to navigate the complexities of miscommunication within text-based interactions. Ultimately, this study enriches our understanding of the nuanced challenges inherent in digital communication practices among college students, contributing valuable insights to the broader discourse on effective communication in the digital age.

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Introduction and Background

The main problem we wanted to investigate was what factors contribute to miscommunication in our target population of college students ages 18-22, since texting (computer mediated communication) is so prevalent in our observed demographic. Our project design observed whether college students preferred texting or live conversations (face-to-face) as a means of communication. While somewhat scarce, previous research on this topic has allowed us to form a basic understanding of miscommunication over text. Studies have shown that the lack of cues, ubiquity, and brevity of interactions during CMC provide a disadvantage compared to FTF communication. Previously, students have felt that texting has both its advantages and disadvantages, and did not indicate a strong preference for CMC or FTF (Kelly et al 2012). Another study found that emojis can serve as speech acts. Emojis possess meaning and intent behind their usage, but their ambiguity over CMC is where miscommunication arises. It was shown that senders and receivers of texts both significantly overestimate the effectiveness of an emoji in conveying meaning, which can lead to miscommunication (Holtgraves 2024). Our research aims to fill the gap left by previous research, especially because much of the research on this topic is over ten years old. We also hope to narrow the scope of such a topic, focusing on a more specific group (college students), rather than a wider range of demographics. Similarly, many studies emphasize emojis and their ability to perform speech acts (Holtgraves 2024). While we do not deny the role emojis play in communication or miscommunication, we also observed the use of acronyms and slang, as well as the lack of FTF multimodal communication such as tone, gesture, and facial expression. Our paper builds on prior research to answer why online miscommunication occurs and whether CMC or FTF is preferred amongst college students ages 18-22 when communicating.

Methods

The method we used to analyze the results of our data was multimodal conversational analysis. One of the factors we mainly focused on was repair. Three components involved in analyzing repair in our data are: 1. The trouble source, or what’s causing the miscommunication, 2. Repair initiation, how the problem is being addressed, and 3. Repair solution, how do the participants solve the problem (Hoey & Kendrick, 2017). In observing instances of miscommunication from our observed community of college students ages 18-22, trouble sources such as lack of tone, social cues, body language, and other multimodal factors, contributed to miscommunication over text or as one respondent stated, “ a common issue between me and my partner is that I’m a blunt texter (no punctuation or emojis) and he’s the opposite. So my texts make him feel like I’m upset at him when in reality I’m just trying to respond as quickly as possible” (anonymous participant). While further analyzing our responses from the Google Form we sent to our participants, repair initiation typically occurred when the people involved in the conversation realized a mishap occurred. Repair solutions transpired as senders and receivers addressed what was miscommunicated and what was actually intended. As in Figure 1, an apology was used and deemed honest because one person admitted the error and explained the problem. Here is another example from our data where these three components of repair can be observed in Figure 1:

Figure 1: Example from our data of using repair in conversational analysis as researched (Hoey & Kendrick, 2017).

Results and Analysis 

Figure 2: Responses to survey question: “How much would you say you prefer texting over in-person interaction?”
Figure 3: Responses to survey question: “How much of a frequent texter are you?”

Our sample population fit our target population, with over 50% of participants being upperclassmen with all having a variety of majors encompassing STEM, the humanities, and social sciences. Figure 2 shows that 52.6% of respondents claimed that they preferred CMC over face-to-face interaction, and figure 3 shows that 42.1% text a little frequently, with 3-5 text conversations occurring a day. All but 2 respondents had listed English as their primary language, at 89% of the responses, the two outliers had put Spanish as their primary language; however, all the examples we received from participants were wholly in English. Participants were prompted to provide an example, either as a screenshot of a CMC conversation or to type out an example. The following question asked respondents to identify the miscommunication, which allowed us to better analyze and understand how they occurred. The majority of participants listed that they were close to their communication partner in their provided example, with 57.8% of respondents identifying their partner as either a friend, significant other, or family member. However, despite this closeness and increased familiarity much of the self-identified reasons for miscommunication arose from a lack of tone, or being too blunt while writing. Some wrote that their communication “[felt] like [it] had sass”, that their blunt texting style makes their significant other believe that they’re “upset at him”, and one respondent wrote that “I can never tell if they’re being sarcastic or genuine”. Other than lack of tone, the second most common misunderstanding was simply one party not being familiar with a word/phrase/acronym being used by their conversation partner.

Discussion and Conclusions

As we examine our results, we can conclude that, based on the demonstrated sample size (initially influenced by previously conducted studies citing technological relevance in effective age groups (Hemmer, Heidi 2009), there is a noticeable preference for in-person, face-to-face communication over computer-mediated conversation. As we had begun to hypothesize how the significance of multimodality comes into play in regard to the interpretation of language through CMC, contextual research, in addition to our data, has allowed us to develop a means of identifying the conditions that allow for the total utility multimodal communication, or a lack thereof within the identified samples. We attempt to examine the aspect of repair within our use of multimodal conversation analysis; recognizing the lack of physical indicators that help to form comprehendible, precise conversations within CMC, followed up with the identification of said misinterpretation of communication acknowledged by both parties, and finally, resolution of both parties being reached upon clarification. When examining the submitted data from a phenomenological lens, we identify the occurrence of these criteria from an individual yet quantified perspective.

References

Hemmer, Heidi (2009) “Impact of Text Messaging on Communication,” Journal of Undergraduate Research at Minnesota State University, Mankato: Vol. 9, Article 5. DOI: https://doi.org/10.56816/2378-6949.1058 Available at: https://cornerstone.lib.mnsu.edu/jur/vol9/iss1/5

Hoey, E.M., & Kendrick, K.H. (2017). Conversational Analysis. Research Methods in Psycholinguistics and the Neurobiology of Language https://pure.mpg.de/rest/items/item_2328034_8/component/file_3513001/content

Holtgraves, T. (2024). Emoji, Speech Acts, and Perceived Communicative Success. Journal of Language and Social Psychology, 43(1), 83-103. https://doi.org/10.1177/0261927X231200450

Kelly, L., Keaten, J. A., Becker, B., Cole, J., Littleford, L., & Rothe, B. (2012). “It’s the american lifestyle! ”: An investigation of text messaging by college students. Qualitative Research Reports in Communication, 13(1), 1–9. https://doi.org/10.1080/17459435.2012.719203

Smith, A. (2011). How Americans Use Text Messaging. Pew Research Center: Internet, Science & Tech; Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2011/09/19/how-americans-use-text-messaging/.

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