Happy April!

First off, if you’re only reading this interested in the title story1 of the newsletter, you may want to just search for “Taylor Swift” in this newsletter and jump forward to that section. It’s a big chunk of this month’s newsletter and is robustly footnoted2 to argue that there’s insufficient evidence to prove that Ms. Swift is responsible for the documented fatalities. This newsletter is primarily designed to help everyday folks use media more mindfully, though, and to that purpose it will cover a range of academic paper summaries that you are of course welcome to skim.

Any readers upset about misdirection are encouraged to post on social media about how mad you are, linking this post so others can be mad with you. (A sense of shared social presence through media can be a driver of wellbeing, as researchers in Shandong, Shanghai and Iowa City demonstrated in papers #1 and #6 covered in this newsletter below.) If that’s not enough, consider becoming a paid subscriber to send me premium hatemail3, though even unpaid subscribers get these “healthy media research summaries” each month for free!

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For everyone else, some newsletter logistics: a core goal of mine this year is to try to build a community around researchers of media and wellbeing, which means I’ve created a new section just for these literature reviews under the label of “The Good, The Bad and the Monthly” -- so if you’re not particularly interested in this as a topic, you can unselect this section in your settings and you’ll still get general posts from me.

But let’s dig into some recent45 papers! I’m going to start by walking through some methodology6 of how the papers are selected, sharing some high-level observations about “wellbeing and media” as a general topic of research.

Some readers may feel like they need explicit permission to choose-your-own-adventure, so…

Paper Overview

As discussed last month, the process I’m using here has stages -- first identifying papers that match the boolean query “media OR wellbeing”, then filtering for the highest semantic score matches, removing papers without abstracts and completing a three-part agentic review. There were 3,896 papers identified this February and, of those, 155 qualified for analysis -- selected as examining the impact of media (as an independent variable) on wellbeing (as a dependent variable).

Of these 155 qualifiers, 65 observed positive effects of some kind and 89 observed negative effects. (One paper, a lit review in the American Journal of Education and Technology, found that social media “is neither uniformly harmful nor beneficial”.)

Part of what I like about this approach of keeping up on papers is that the authors cover a wider range geographically than I would come across in a more organic method.

Just scanning the map, a few curiosities pop out to me.

• Within the US, it does seem like the liberal northeast is more likely to publish about harms of media whereas the interior of the country seems to publish about its benefits.

• Europe also seems more likely to publish on this topic, with Western Europe’s research generally more critical of media’s effects than Eastern Europe’s.

• Indonesia publishes so much about this topic -- both in total output and on a per capita basis. (Three papers in today’s digest come from Indonesia.) Indonesia does not overindex on wellbeing papers in other categories I monitor, so this trend is notable, and I hope to investigate it further.

• There are considerably fewer publications from Africa and South America than we would expect given their population size.

This general trend of media-critical papers coming out of the US and Europe aligns somewhat with responses from a question I ask in the Mic Check Media Daily Global Omnibus7. Here we find that residents of Europe and the USA are among the regions where residents are most likely to believe that their media habits hurt their wellbeing.

Topic-wise, “social media” remains a frequent focus for these studies -- over half of the studies identified make it their target, 80 of the 155 identified papers. Of those 80, nearly four in five (77.5%) identify negative effects, ranging from FoMO, burnout, reduced physical activity, addiction, problematic use, reduced self-esteem and more. On the positive side, some studies found social media encouraging help-seeking, improving mood, increasing interest, increasing informedness and reducing stigmas.

I’m working on some new ways to visualize the social media results in particular, and to make it easier to understand the specific operationalizations examined, but for now you can note how social media dominates the “large affected population size” of my platform/effect chart.

Enough about methodology -- let’s dig into some of my favorite papers from the month!

What’s Good and Bad in Media Research: February 2026

Rather than having separate “good” and “bad” sections as I did last month, I’m experimenting with a back-and-forth flow -- hopefully this doesn’t leave readers with such a depressing vibe by ending with a parade of horribles. We’ll both start and end on positives!

Good: Podcasts!

In a survey of young adults, Shandong University’s Weiwei Li found that not only is listening to podcasts correlated with higher wellbeing, but that the mechanism at play is that podcasts create a greater sense of social presence and social support. I know podcasts help my wellbeing personally, and I think I can see Li’s model at work in my own listening habits -- connecting me with fellow listeners as well as generally keeping me company.

The paper is peppered with open-ended responses trumpeting appreciation for podcasts and the communities they create. Though the specific programs they mention are different from those we have in the US, I think the same principles likely apply here!

Full paper: “Podcast Listening, Perceived Social Presence, Perceived Social Support, and Subjective Well-Being Among Chinese Young Adults: Sequential Explanatory Mixed Methods Study”8

Bad: ChatGPT!

Researcher Arwa H. Arab, in Jeddah, Saudi Arabia, found that more time using ChatGPT was predictive of depression in a regression analysis of survey data from 200 students at King Abdulaziz University. Interestingly, though, a question about “comfort discussing mental health with ChatGPT” predicted lower levels of depression and lower levels of anxiety, suggesting a range of possible relationships between AI and wellbeing -- potentially driven by different user characteristics or usage patterns.9

Full paper: “Between coping resource and risk factor: ChatGPT and social media use in relation to anxiety, depression, and somatization”

Good: Mental Health Media Guidelines

The World Health Organization released specific media guidelines in 2023 to prevent deaths potentially attributable to media coverage. A team of researchers across the US, Canada and Australia reviewed 15 studies across 11 countries that examined the impact of similar media guideline efforts, finding that these government-led initiatives significantly increased compliance with best practices.

While the study didn’t demonstrate that these guidelines save lives, the authors suggest this is because only 3 studies in their meta-analysis included death rates. Their literature review, though, is persuasive in referencing other papers that have demonstrated the impact of media coverage on death rates. As such, I’m personally inclined to believe in their broader wellbeing impact.

Full paper: “Impact of Media Guidelines on Sxxxide-Related Reporting Quality and Sxxxides.”10

Bad: The Netflix Film “The Most Beautiful Girl in the World”!

In Surabaya, Nancy Anggita and Ade Kusuma conducted a set of interviews to find that the Indonesian rom-com The Most Beautiful Girl in the World can reinforce beauty standards for viewers.

Specifically, they conducted long-form, open-ended interviews with media professionals and analyzed them with a framework called Stuart-Hall Reception Theory (SHRT). This approach classifies “readings” of a film as either dominant, negotiated or oppositional, based on whether what is understood from a film is aligned with the intended/mainstream message.11 They found that nearly all interviewees (4 out of 5) accepted or partially accepted the beauty myth ideology the researchers identified in the film (“dominant” and “negotiated” reading positions), with only one interviewee taking a critical view (or “oppositional” reading position).

I probably would have preferred a larger sample size with a more structured survey instrument, because I have many more questions. What film characteristics are most conducive to oppositional readings? And what viewer characteristics drive dominant, negotiated and oppositional readings? Ultimately, though, I enjoyed seeing a theoretical tool applied in an empirical setting, and I’ll make sure to not watch The Most Beautiful Girl in the World!12

Full paper: “Penerimaan Pekerja Media terhadap Mitos Kecantikan pada Film The Most Beautiful Girl in the World di Netflix”13

Good: “Deinfluencers”!

A study by Florida Gulf Coast University researchers Gina A. Tran, Youngok Sunny Song and Khaled Aboulnasr explores a subset of anti-consumption content creators (whom they call “deinfluencers”) that are effective in shaping anti-consumption attitudes within their audiences.14 (Or that’s what the abstract says; the results and discussion are paywalled!15)

I wanted to point this paper out anyway because I’ve worked on many, many ad effectiveness studies as a market researcher, and this might be the first “reverse ad effectiveness study” I’ve seen, and if the conclusions truly are supported by their data and methodology, it shows the design of our media system can shape our values and beliefs about consumption.

Full paper [paywall]: “Power to the deinfluencers: how parasocial relationships drive anti-consumption among young consumers”

Bad: Smartphones!

For those searching for it: “Taylor Swift”!

A Harvard Medical School paper making the rounds is media catnip because it could implicate Taylor Swift in the deaths of 500+16 people over the past decade. More generally, though, it found that the release of major, highly publicized albums on streaming services is correlated with an increase in traffic fatalities. The findings suggest that using smartphones to play music while driving is a serious public health concern worthy of deeper consideration. There’s honestly a lot that is interesting in this paper, so I’m going to spend a bit more time with it.

The methodology focuses on the “Top 10 Most Streamed Albums in a Single Day from 2017 through 2022”, which in addition to the 3 Taylor Swift albums includes 2.5 Drake albums as well as albums from Bad Bunny, Kendrick Lamar17, Harry Styles and Kanye West. This quasi-experimental design is very cool, but there are a couple of reasons why I think we can’t attribute traffic fatalities to any particular artist. And in describing these reasons, it is my hope to deliver on the promise of my clickbait headline and exonerate these artists from all suspicion.18

“I Look in[to] People’s Windows[’ Effects on P-Values]”

(Or: “[Publication Incentive Impact on Academic] Paper [Postu]Rings”)19

The crown jewel statistic of the paper is a p-value of 0.01 when comparing album release days to non-album release days, suggesting that it is extremely unlikely this effect is due to chance. But I’m going to cast some doubt on this statistic by examining three window choices.

First, there’s the choice of the 21-day comparison window. This selection is explained as providing “the same weekday one week before and after the release as well as the weekends immediately before and after”, but a 15-day window (±1 week) seems to be the most obvious choice containing both proximate weekends and both proximate weekdays.20 A natural second choice to strengthen the sample size would be a robust 29-day window -- including two full weeks before and after. The 21-day window (±10 days) feels somewhat arbitrary. The elegance of Figure 2A, which shows the fatality statistics for each day in the focus window, is driven in part by the fact that the “norm” is set to 10 days prior to release. If you also include non-centered comparison window possibilities like “past week”, “past two weeks”, “past three weeks” or even “past four weeks”, you can easily arrive at 6 alternate comparison windows that could seem more intuitive than ±10 days.

Second, there’s the choice of the treatment window -- that is, that only the date of release would be considered “elevated” in terms of risk. Their own Figure 1 clearly shows elevated levels of listening for on the second day (the first full day of album availability), and slightly elevated streaming rates through the first week. The release day is probably the most obvious choice here, but there are a couple of justifiable approaches one could take.

Third, there’s the choice of study window. The study focuses on data between 2017 and 2022, and the reasoning for this window is not clarified. As someone who is writing about February papers in April, I understand that there are limits for our ability to incorporate the most recent information into analyses, but the 2023 FARS data has been available for about a year so I’m naturally curious whether they tried to include it. (The study window starting in 2017 is, as far as I can tell, because that’s how far back this dataset from Spotify goes.) Further, there is a choice about how to apply this study window -- taking the top ten albums across the entire window rather than the top 20 or the top 25. Another approach could be selecting the top albums from each year, as Spotify adoption drove increases in usage over this period. Thus, their top ten skews toward later years, with half of them from the latest (2022) and none21 from the earliest (2017). Of the three window choices, this seems the most defensible, but the optionality here is still important to note.

Taken together, these three choices around these three windows create important context for viewing the 0.01 p-value. In a single prespecified analysis, a p-value of 0.01 would be unlikely to arise by chance. But acknowledging 7 possible comparison windows, 3 possible treatment windows and 2 possible study windows means we should be less surprised if one of the 4222 combinations of these windows might have such a low p-value.

To be clear, there’s no definitive proof that these researchers tried other window combinations in their research process, but some level of data exploration like this is normal in the research process. When it is done excessively, it is called “p-hacking”, and its widespread practice in published research is a driver of “the replication crisis” -- that the findings of many papers do not seem to hold up to scrutiny. The findings would be more convincing if other windows were demonstrated to be consistent.

Here at Middling Content, we often reflect on how media platforms can distort truth to serve platform incentives. This is not just a feature of the latest social media platforms; it has also long been a challenge in published research. I don’t blame academics, who face intense pressure in seeking rare paid positions and grant opportunities. And I don’t blame individuals who are drawn to morbidly certain conclusions in papers like this one. This is our nature. When we drive past an accident, we slow our vehicle to inspect the wreckage. We are inherently dark creatures who want to contemplate the death count of “All Too Well (10 Minute Version)”.23

“[More Album] Hits [May Have] Different [Fatality Rates]”

(Or: “I Know [Other] Places [That Suggest All Artists Aren’t to Blame]”)24

Of course, it’s possible that the analysis replicates across various windowing choices or with new data in the future. Even in that case, though, I think these findings do not condemn any particular artist.

To explain why, it’s helpful to ask “what is really being measured here”. On the podcast The Argument25 released last Thursday, Matt Yglesias made a good point that this is likely a substitution effect. I’ll go further: in theory, an album that is all bangers end-to-end26 would not require people futzing with their phones. When a new album is released, though, people don’t have set playlists and have to actively curate; it’s perhaps this higher likelihood of track-skipping that creates the observed effect.

Track-skipping27 would be an interesting explanatory mechanism because this presumably varies considerably across albums and artists. I expect that these top ten albums have varied levels of skipping, and that means it’s possible that some artists on this list are less responsible for the observed increase in fatalities. There’s variation here. Some albums (and thus some artists) are conceivably innocent.

My suggestion for a follow-up study28: do new albums with more skipped tracks2930 cause more deaths? Has single-driven culture not just meant the death of the album as an artform31 but also the death of ~50 people per tentpole album release? The music industry should take note: “filler tracks are killer tracks”.32

“You’re On Your [Phone], Kid”

(Or: “I Wish You Would [Not Use Smartphones While Driving]”)

(Or: “Getaway [From Your Phone While In The] Car”)

Rather than seeing this study as a critique of uneven albums, though, the intended (normie) reading of the paper would be that smartphones are seriously dangerous for drivers.33 The days when our favorite artists release albums are days when we are more likely to switch from our usual driving routine -- a long-form podcast, a playlist or a radio station -- to instead explore something new that will require more intervention, but we certainly choose to skip tracks on other days as well. No matter what you’re listening to, take seriously the risks of even minor and brief smartphone use. Letting that subpar song play out until the end might just save your life.

(You can share this newsletter with a Swiftie to trick them into reading a public service announcement about smartphones and driving! And if you are a Swiftie and had this forwarded to you: someone cares about you and wants you to be safe!)

Full paper: “Smartphones, Online Music Streaming, and Traffic Fatalities”34

Good: Television Public Health Campaigns!

Researchers Musa Mathias and Gana Hope conducted a survey in central Nigeria’s Nasarawa State, finding that public health television campaigns were effective at sharing information with citizens in a broadly trustworthy manner.

My favorite stat was that 95.7% of those surveyed considered the info from the campaign to be “authentic”, with only 2.3% disagreeing with that statement. Though I can’t know for sure, I do wonder if the trustworthiness of TV campaigns in other parts of the world has eroded? That stat sounds so high to me. Further, I’d be interested in how wide the gap is between the trustworthiness of television and of other media.35 As television becomes less central in our media landscape, public health communicators may lose a valuable, relatively high-trust platform.

Full paper: “Attitude and Perception of Nasarawa State Residents towards COVID-19 Television Awareness Campaign”

Bad: Bullet Screen Comments!

If you’ve ever watched content on Bilibili.com36 or similar platforms, you’re probably familiar with “danmaku” or “bullet screen comments”, text written by other viewers that flies across the screen during your viewing. Mingyue Zhang, Lei He and Haipeng (Allan) Chen found that excessive use of these “bullet screen comments” can reduce enjoyment of videos. A moderate quantity, though, could enhance experience quality and increase the “likes” of a video -- a classic “middle path” to wellbeing that we like to see here at Middling Content.37

As for the sweet spot at which this feature tips from useful to annoying, I was not able to view this paper in full, so I’m going off of the abstract here as well. So use sparingly and with caution!

Full paper [paywall]: “Socially engaged or informationally overwhelmed? Effect of bullet-screen comment quantity on video-liking”

Good: “Film Therapy”!

Back to Indonesia, where an empirical evaluation of “film therapy”38 found it increased pro-social behavior in students. A team of researchers at Bandung’s39 Universitas Nasional Pasim (Balqis Arwa Aziizah, Muhammad Fikri Izzuddin, Raden Aurora Sativa Hawa Gantara and Sri Mulyeni) found in a pre/post survey that the short-film Indifferent40 increased self-reported empathy on a battery of five-point Likert scales.

(While the English-language abstract claims they didn’t find significance, Table 5 clearly shows “signifikansi” of 0.003 -- making this the first abstract I’ve ever seen that undersells the research it summarizes, truly worthy of some kind of award!)

Full paper: “Efektivitas Terapi Film dalam Meningkatkan Empati”

Bad: AI-Generated Visual Content!

A team of researchers at Christ University in Bangalore conducted a literature review of studies, finding that AI-generated visual content reduced self-esteem with pronounced negative effects for young people, the economically disadvantaged and women. Other negative effects they found in recent studies observed that AI images can distort identity, promote social comparison, and promote dependency on AI visuals for psychological validation.

Much of the current academic perspective is depressing in this way, but there were a few positive relationships noted.41 To me, the most interesting of which were a couple papers from 2024 and 2025 that discuss the potential for AI-generated visuals in therapeutic contexts.

The balance of papers they draw on gives a range of perspectives. To some degree, I’m not so sure we can draw as much from papers evaluating generative AI visual content from as far back as 2023, as this technology changes so rapidly. And yet, with many papers from the past year, I appreciate this overview!

Full paper: “A Systematic Review on the Psychological Implications of AI-Generated Visual Content for Users’ Self-Esteem and Self-Perception”

…and Good?: AI-Generated Visual Content!

Too recent for the above literature review, though, was a new study about AI-generated visual content from a trio of researchers at Universitas Pendidikan Ganesha in Northern Bali. I Gusti Ayu Agung Mascintya, Putu Aditya Antara and Rendy Setyowahyud found that the AI video creation tool Powtoon could be used to localize children’s educational content, which improved student mental health.

It’s an interesting notion when much of the conversation about AI video is negative, but there are a couple missing pieces of the analysis before drawing conclusions: (1) I’d like to see measures for both educational outcomes and mental health outcomes, and (2) the study could benefit from a comparison group of traditional (non-localized and non-AI) content. The upside of representational impact may indeed outweigh the downside of the “slop” factor -- but without a comparison it’s hard to say!42 That said: still an interesting angle on the potential benefits of AI-generated visual content!

Full paper: “Pengembangan Video Pembelajaran Digital dengan Menggunakan Powtoon Berbasis Kebudayaan Bali dalam Menstimulasi Kesehatan Mental Anak”43

Tune In Next Month

I added a few new visuals and statistics versus last month, but there’s still lots to improve.

My focus for the next couple months will be on the following.

• Taxonomy Improvements: Some papers cover multiple applications, so it’d be better to treat app-specific studies as both about the particular app and about broader categories. The study about smartphones and traffic fatalities, for example, should count not just as about smartphones but also about Spotify and streaming audio.

• Broadening Scope: My automated paper identification process misses some papers that are a good fit for this, so I’m looking to improve coverage with more deliberate testing against known examples.

• Social Media: It has been huge in the news lately and dominates research about media and wellbeing, but I haven’t really touched it because there’s so much to dig into. I’m hoping to have a “special edition” on social media in the near future.

• Shorter: This newsletter needs to be so much shorter. For me. For you. Next time will be shorter. It will help with timeliness.

Lastly, special thanks to my former research advisor who sent me a paywalled paper I couldn’t access last month! I will try to find some kind of institutional affiliation to access more of these papers in the future; in the meantime, I’ll continue to do my best with just abstracts!

And if you ever want to nominate a specific paper for me to cover, you can send me a direct message and I’ll try to include it in its respective month!

See you later this April when I’ll dig deeper into the findings of March!44

And if you are a researcher in media and wellbeing, or interested in using media more mindfully, please subscribe to The Good, The Bad and The Monthly and send me nominations for future papers!

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I’ve counted at least 4,774 papers about how the media impacts wellbeing last year. How many did you read? How many did you hear about secondhand?1

It’s hard to keep track of all the research that gets published, and I myself haven’t followed as closely as I’d like to. I’ve written about how media incentives aren’t always aligned with the interests of audiences, and science journalism is one of the clearest examples of this.

This challenge is downstream of three other challenges:

• Reading Papers Is Time-Consuming

• Assessing Papers Requires Expertise

• There Are Lots and Lots of Papers

Last year, I created a system to track papers published related to my particular focus on media and wellbeing, and I’m hoping to utilize this to help non-specialists (with still some level of quantitative background) follow what’s going on in the research literature. And by covering both media harm risks and healthy media opportunities, my goal is to avoid the clickbait doomscroll aesthetic of other newsletters.

Since my personal new-year-to-new-birthday interregnum has elapsed, I’m going to apply some of my own reflections to make Middling Content better. This will be a monthly newsletter that tries to help you understand what might be good and what might be bad in the world of media.

A few values are going to guide this project:

• Focus: I think the newsletter last year had become a bit sprawling, and I’d like to have a core that helps me build toward a specific objective. I may still have a long-form post every once-in-a-while, but this monthly structure will give a more stable architecture.

• Community: Whereas the newsletter was mostly pushing my own ideas and reflections in 2025, I also would like to help connect scholars studying this space with each other.

• Usefulness: While maintaining more of a reflective travelogue has been fun, it’s more obvious how a digest like this can help people make everyday choices about how to interact with various media tools -- and even to design their own media tools as AI empowers more direct management of our media and information pipelines.

And thus I’m introducing “The Good, The Bad and The Monthly”: a breakdown of recent academic articles on media and wellbeing that attempts to distill all these insights into something actionable. I hope to keep this running through the year at least, and if people find it useful or interesting I can continue it further beyond that.

Some extra on methodology since this is the first post, but feel free to skip ahead to “What’s Good”, where I talk about findings that affirm media doing good in the world. (Or just skip ahead to the “Media Impact on Wellbeing…” biplot if you’d like!)

How to Boil the Ocean

There’s a lot of sloppy thinking on what media is good and bad. It’s fun to be sloppy sometimes, but it’s important to occasionally be non-sloppy too.23 (Often, even.) So I’ve created a four-stage pipeline that I’m using to process papers on media and wellbeing: find, filter, finesse & assess.

Here’s what that can look like.

1. Find. Each week I query Semantic Scholar identifying papers that relate to both “media” and “wellbeing”.

2. Filter. Calculating semantic distance toward both “media” and “wellbeing” as topics within the papers. (See my essay on “Law & Quoter” for an explanation of semantic distance as a concept, particularly the section on Wordspace.) Top 200 papers with abstracts progress to the next round.

3. Finesse. First, a chat completion review to confirm that both “wellbeing” and “media” are explored in the publication, and at least one of the two is central to its research question and conclusions. Then, a second chat completion review to confirm that the paper specifically looks at media impacting wellbeing.

4. Assess. A LLM-supported style transfer into structured data, identifying if and how each article observes a specific media type’s impact on wellbeing. Impacts are further quantified in a synthetic maxdiff exercise to estimate relative valence of impacts.

Ultimately, the exercise produces a dataset of media types and their evaluated impact on wellbeing.

From here, I can pick a few particular papers to highlight.

Let’s take a stab at this to show what that could look like!

What’s Good?

Researchers Jan Boehmer and Kyriaki Kaplanidou calculated that the TV program Welcome to Wrexham increased employment, per capita GDP and mental health within the city of Wrexham, Wales. (Full paper: Did Deadpool Save a City? The Social and Economic Impact of “Welcome to Wrexham”.)

An experimental study conducted by researchers at Lafayette College and the Economic and Social Research Foundation in Tanzania found listening to 90-second radio dramas nearly doubled willingness to report intimate partner violence. (Full paper: The effect of radio dramas on willingness to report intimate partner violence.)

Researchers at the School of Social and Behavioral Sciences in Nanjing University found that playing video games with nature settings increased the likelihood of donations to environmental causes and support for pro-environmental policy. (Full paper: Techno-Biophilia Design in Video Games and Its Positive Social Outcomes.)

Researchers Fiona Chalk and Chillonda Agyeman found that music can reduce reliance on medication for dementia patients. (Full paper: The role of music in supporting people with dementia in acute hospitals.)

Interviews conducted by Ikechukwu Williams Eke and Abiodun Salawu found that BBC News Igbo increased prestige and pride associated with the language of Igbo in Nigeria. (Full paper: Impact of digital media on language revitalization: the case of BBC News Igbo.)

Cici Situmorang, Lia Amalia Ramadhani, Iqna Nur Fadilah and Raffa Fahdiaz Romadhon at Institut Prima Bangsa attributed increased learning motivation and decreased anxiety in educational environments to the Sabrina Carpenter “Nobody’s Son” music video.4 (Full paper: “Meningkatkan penguasaan kosakata Bahasa Inggris melalui video lagu Nobody’s son”)

Food vlogging content was found to increase “cultural curiosity” and visiting intention by researchers Md. Salamun Rashidin, Babak Taheri, Giacomo Del Chiappa and Sara Javed. (Full paper: From cultural curiosity to culinary sustainability: how social food reels foster food culture preservation.)

What’s Bad?

On the other hand, researchers at University of Pennsylvania and University of Delaware found that viewers were more likely to eat a cookie instead of an apple after watching food vlogging content that includes sponsorships. (Full paper: Your media diet is impacting your actual diet: The effects of influencer “What I Eat in a Day” YouTube videos on influencer perceptions and nutrition behaviors.)

A literature review from Ibrahim Taylan Dortyol, Merve Nalbant, and Asli Guven at Akdeniz University explored all the various ways that selfies impact body image. It’s paywalled but I’ll go halfsies with anyone interested. (Full paper: Selfie Behaviours and Body Image Concerns: A Systematic Literature Review and Future Research Directions.)

Xiaofan Yue and Xin Cui at Huangshan University found that “emotion enhancement motives” toward binge-watching makes addiction more likely. (Full paper: Binge-watching addiction as an emotion regulation way of coping loneliness.)

Researchers at Ramathibodi Hospital, Siriraj Hospital and the Institute of Child and Adolescent Mental Health in Bangkok, Thailand found that watching short-form videos about parenting was correlated with lower scores on evaluations of parenting knowledge -- whereas reading online materials was correlated with higher scores.5 (Full paper: The Association Between Media Use for Parenting Information (MUPI) and Parenting Knowledge, Attitudes and Practices Among Parents of Children Aged 6-19 Years.)

Kristine Schönhals, Christopher Lalk and Silja Vocks at Osnabrück University found that “fitspo” and “thinspo” content -- content that glamorizes fit or thin body types -- decreased happiness and body appreciation while increasing body dissatisfaction. (Full paper: Fitspiration, Thinspiration, Body Positivity, and Body Neutrality Contents on Image-Based Social Media: Associations With Body Image, Mood, Self-Esteem, and Disordered Eating Behavior in Women With and Without Self-Reported Eating Disorders-An Ecological Momentary Assessment Study.)

Researchers at Universitas Tarumanagara surveyed Gen Z LinkedIn users and found that more focus on maintaining their digital identity was correlated with more feelings of “social comparison”, which can be correlated with anxiety or insecurity. (Full paper: MODERASI KEBIJAKSANAAN DALAM HUBUNGAN IDENTITAS DIGITAL DAN PERBANDINGAN SOSIAL PADA GENERASI Z PENGGUNA LINKEDIN.)

Researcher Dhelittya Finaliyani Putri and the Islamic Communication and Broadcasting Department examines Italian brainrot and speculates on how it may impair concentration of students.6 (Full paper: “Fenomena Anomali Konten Digital dan Dampaknya terhadap Anak-anak”.)

And of course there were 124 papers on the impact of social media -- the majority of them finding negative effects and correlations, with 34 seeing positive effects and 89 seeing negative effects.

I need a dedicated methodology just to think about the social media research, to be honest. I don’t really have that yet. I’m working on it.

What’s Next?

This is a first pass, and since I have this constructed as a data pipeline it’ll be easier to build on this in future months.

I think there are three main areas I can continue to improve.

• Timeliness. It’s March and I’m writing about January! It does take a little time for academic articles to be uploaded to source databases, so I’m okay with leaving a little time between “end-of-month” and the next post, but I’m hoping to be closer to “a week or two after the month ends” for my publication cadence.

• Density. I think I’m losing too much value in the distillation process, and I would like to increase coverage of agentic-filtering and have a better measure of “methodology quality” -- differentiating pure experiments from natural experiments from correlational analysis, weighing concerns about external or internal validity, contemplating sample sizes.

• Accessibility. I’ve been told that some of you find it easier to listen to a podcast than to read a newsletter, and so I’m trying to figure out if I can use a different format. I also am trying to incorporate more visuals and scorecards.

As always, I’m open to your thoughts on how a breakdown of a month’s worth of research can be valuable to you; if you like or dislike a certain approach, please let me know!

Thanks for following this journey as always!

Stay attuned!

Harry