Brands have caught on and started engaging on our platform, but that doesn’t guarantee it’ll be captured by the LLMs. The search ecosystem is still under construction, and while we don’t have all the answers yet, what we know so far is that showing up on Reddit does correlate with an increase in LLM mentions. https://www.adweek.com/media/the-secret-to-brand-visibility-in-the-llm-era/
TBPN’s newest sponsors include the New York Stock Exchange (NYSE), Shopify, Plaid, CrowdStrike, AppLovin, and others. https://www.adweek.com/media/tbpn-advertisers-shopify-plaid-applovin-nyse/
Unveiled at CES, Max Campaign aims to win performance ad dollars while offering brands more transparency. https://www.adweek.com/media/reddit-max-campaign-media-buying-ces/
The US has adopted more of a middle ground approach, essentially letting private companies decide what they wanted to do. Daymude and his co-authors wanted to investigate these markedly different approaches. So they developed a computational agent-based simulation that modeled how individuals navigate between wanting to express dissent versus fear of punishment. The model also incorporates how an authority adjusts its surveillance and its policies to minimize dissent at the lowest possible cost of enforcement.
“It’s not some kind of learning theory thing,” said Daymude. “And it’s not rooted in empirical statistics. We didn’t go out and ask 1000 people, ‘What would you do if faced with this situation? Would you dissent or self-censor?’ and then build that data into the model. Our model allows us to embed some assumptions about how we think people behave broadly, but then lets us explore parameters. What happens if you’re more or less bold? What happens if punishments are more or less severe? An authority is more or less tolerant? And we can make predictions based on our fundamental assumptions about what’s going to happen.”
According to their model, the most extreme case is an authoritarian government that adopts a draconian punishment strategy, which effectively represses all dissent in the general population. “Everyone’s best strategic choice is just to say nothing at this point,” said Daymude. “So why doesn’t every authoritarian government on the planet just do this?” That led them to look more closely at the dynamics. “Maybe authoritarians start out somewhat moderate,” he said. “Maybe the only way they’re allowed to get to that extreme endpoint is through small changes over time.”
Daymude points to China’s Hundred Flowers Campaign in the 1950s as an illustrative case. Here, Chairman Mao Zedong initially encouraged open critiques of his government before abruptly cracking down aggressively when dissent got out of hand. The model showed that in such a case, dissenters’ self-censorship gradually increased, culminating in near-total compliance over time.
But there’s a catch. “The opposite of the Hundred Flowers is if the population is sufficiently bold, this strategy doesn’t work,” said Daymude. “The authoritarian can’t find the pathway to become fully draconian. People just stubbornly keep dissenting. So every time it tries to ramp up severity, it’s on the hook for it every time because people are still out there, they’re still dissenting. They’re saying, ‘Catch us if you dare.’”
https://arstechnica.com/science/2025/12/the-science-of-how-and-when-we-decide-to-speak-out-or-self-censor/
This story was originally published in On Background with Mark Stenberg, a free, weekly newsletter that explores the key themes shaping the media industry. You can sign up for it here.
The media industry spent much of 2025 reacting: to AI-driven traffic volatility, to shifting advertiser expectations, and to the collapse of familiar playbooks.
Looking ahead to 2026, a dozen media executives told ADWEEK they anticipate a year defined less by experimentation and more by sorting: what scales, what doesn’t, and what audiences and marketers will actually reward in an AI-saturated environment.
Across the industry, the predictions converge around a few core ideas.
https://www.adweek.com/media/2026-media-predictions/
Like most tools, generative AI models can be misused. And when the misuse gets bad enough that a major dictionary notices, you know it’s become a cultural phenomenon.
On Sunday, Merriam-Webster announced that “slop” is its 2025 Word of the Year, reflecting how the term has become shorthand for the flood of low-quality AI-generated content that has spread across social media, search results, and the web at large. The dictionary defines slop as “digital content of low quality that is produced usually in quantity by means of artificial intelligence.”
“It’s such an illustrative word,” Merriam-Webster president Greg Barlow told the Associated Press. “It’s part of a transformative technology, AI, and it’s something that people have found fascinating, annoying, and a little bit ridiculous.”
To select its Word of the Year, Merriam-Webster’s editors review data on which words rose in search volume and usage, then reach consensus on which term best captures the year. Barlow told the AP that the spike in searches for “slop” reflects growing awareness among users that they are encountering fake or shoddy content online.
Dictionaries have been tracking AI’s impact on language for the past few years, with Cambridge having selected “hallucinate” as its 2023 word of the year due to the tendency of AI models to generate plausible-but-false information (long-time Ars readers will be happy to hear there’s another word term for that in the dictionary as well).
The trend extends to online culture in general, which is ripe with new coinages. This year, Oxford University Press chose “rage bait,” referring to content designed to provoke anger for engagement. Cambridge Dictionary selected “parasocial,” describing one-sided relationships between fans and celebrities or influencers.
As the AP points out, the word “slop” originally entered English in the 1700s to mean soft mud. By the 1800s, it had evolved to describe food waste fed to pigs, and eventually came to mean rubbish or products of little value. The new AI-related definition builds on that history of describing something unwanted and unpleasant.
https://arstechnica.com/ai/2025/12/merriam-webster-crowns-slop-word-of-the-year-as-ai-content-floods-internet/
Meta’s acquisitions of Instagram and WhatsApp, both completed over a decade ago, did not illegally disadvantage competitors, a federal judge ruled on Tuesday. As a result of the decision, the social media giant will not be forced to divest either of the assets.
The decision by Judge James E. Boasberg of the U.S. District Court of the District of Columbia caps off a battle between the Federal Trade Commission and Meta that began with a complaint filed in 2020. The government alleged in the case that Meta’s purchase of Instagram and WhatsApp evidenced the company’s strategy to systemically weed out competition in the social media space.
https://www.adweek.com/media/ftc-meta-ruling-antitrust-instagram-whatsapp/
The next time you encounter an unusually polite reply on social media, you might want to check twice. It could be an AI model trying (and failing) to blend in with the crowd.
On Wednesday, researchers from the University of Zurich, University of Amsterdam, Duke University, and New York University released a study revealing that AI models remain easily distinguishable from humans in social media conversations, with overly friendly emotional tone serving as the most persistent giveaway. The research, which tested nine open-weight models across Twitter/X, Bluesky, and Reddit, found that classifiers developed by the researchers detected AI-generated replies with 70 to 80 percent accuracy.
The study introduces what the authors call a “computational Turing test” to assess how closely AI models approximate human language. Instead of relying on subjective human judgment about whether text sounds authentic, the framework uses automated classifiers and linguistic analysis to identify specific features that distinguish machine-generated from human-authored content.
“Even after calibration, LLM outputs remain clearly distinguishable from human text, particularly in affective tone and emotional expression,” the researchers wrote. The team, led by Nicolò Pagan at the University of Zurich, tested various optimization strategies, from simple prompting to fine-tuning, but found that deeper emotional cues persist as reliable tells that a particular text interaction online was authored by an AI chatbot rather than a human.
In the study, researchers tested nine large language models: Llama 3.1 8B, Llama 3.1 8B Instruct, Llama 3.1 70B, Mistral 7B v0.1, Mistral 7B Instruct v0.2, Qwen 2.5 7B Instruct, Gemma 3 4B Instruct, DeepSeek-R1-Distill-Llama-8B, and Apertus-8B-2509.
When prompted to generate replies to real social media posts from actual users, the AI models struggled to match the level of casual negativity and spontaneous emotional expression common in human social media posts, with toxicity scores consistently lower than authentic human replies across all three platforms.
To counter this deficiency, the researchers attempted optimization strategies (including providing writing examples and context retrieval) that reduced structural differences like sentence length or word count, but variations in emotional tone persisted. “Our comprehensive calibration tests challenge the assumption that more sophisticated optimization necessarily yields more human-like output,” the researchers concluded.
https://arstechnica.com/information-technology/2025/11/being-too-nice-online-is-a-dead-giveaway-for-ai-bots-study-suggests/
Your voice is your most underused business asset.
Founders who post at least 10 times a year on LinkedIn:
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https://www.searchenginejournal.com/linkedin-posts-into-larger-sales-deals/559430/
There’s big news to come out of Monday’s edition of ADWEEK’s Brandweek in Atlanta: Viral Nation is announcing the launch of its new proprietary measurement framework, The Culture Quotient, CQ.
The framework is a first-of-its-kind offering for measuring cultural relevance and business impact, according to Viral Nation. CQ utilizes advanced AI and social intelligence to measure how a brand is represented in culture, translating social signals—such as engagement growth, conversation volume, and sentiment—to tangible results.
“The Culture Quotient is the next evolution of measurement. It’s benchmarkable, trackable, and tied directly to business outcomes,” said Joe Gagliese, Viral Nation CEO. “It’s a framework that tells brands not just how loud they are, but how deeply they resonate in culture.”
CQ is looking to replace traditional social metrics like earned media value (EMV) and impressions, which only measure visibility, not value. According to Viral Nation, these measurement methods show reach rather than resonance.
“Culture drives business,” said Richelle Batuigas, Viral Nation’s executive vice president, business intelligence and data strategy. “When brands connect authentically to what people care about, we see it in the data—consideration, loyalty, sales. The Culture Quotient finally gives marketers a way to see and scale that connection.”
With social media as the main playground for relevance, Viral Nation notes that its data consistently shows that when people perceive a brand as culturally relevant, it drives everything from consideration to purchase intent and loyalty.
According to the company, cultural connection is the new currency of trust.
“We realize [EMV] is a metric that doesn’t actually give us true insight into performance or cultural relevance,” Batuigas told ADWEEK. “That’s why we want to do away with it and introduce this new score that actually gives you true insight.”
For Batuigas, the value comes down to measuring the impact of all communication and going beyond quantity.
“It’s about thinking more deeply about the quantity and the quality, and also about how you arrived at those outcomes so you can drive better results and better connections,” Batuigas said.
https://www.adweek.com/media/viral-nation-launches-the-culture-quotient-measurement-at-brandweek/