WSJ What’s News · Episode
How ICE Is Weaponizing Social Media Against Its Critics
The agency says its surveillance program searches for threats to agents, but critics see free-speech infringements.
ICE’s Internal Watchdog Is Now Investigating Online Critics
The Office of Professional Responsibility has opened more than 100 cases over what ICE officials call “incidents of doxing and threats” against ICE employees.
US Department of Homeland Security has reportedly demanded personal information about ICE's critics from Discord, Reddit, Google, and Meta—and at least 3 of those platforms have complied
DHS has issued hundreds of subpoenas to major online platforms to obtain the names, email addresses, and phone numbers of accountholders who criticize ICE.
ICE Office Of Professional Responsibility Ditches ICE Oversight, Starts Hunting Down ICE Critics
ICE has already been operating like a paramilitary kidnapping squad. Officers roam through neighborhoods, stake out hardware store parking lots, and even occasionally enjoy some ethnic food just so…
How Hackers Are Fighting Back Against ICE
A few enterprising hackers have started projects to do counter surveillance against ICE, and hopefully protect their communities through clever use of technology.
AI in journalism: Live tracker of scandals and mistakes
AI in journalism: Live tracker of mistakes and mishaps from the Mississippe Free Press to the New York Times.
Elena Rossini 🌈 (@_elena@mastodon.social)
SCOOP: #WSocial is doctoring metrics on its homepage, inflating the number of comments on posts by prominent people on its network. I suppose a more accurate tagline for them should be "Trust your feed?" My article about it: "W Social, Fictional Metrics and the Beauty of Open Data" 🔗 : https://blog.elenarossini.com/w-social-fictional-metrics-and-the-beauty-of-open-data/ #blog #BigTech #EUBigTech #TEP #TrustedEuropeanPlatforms #TrustYourFeed
ICE Is Paying a Controversial AI Firm to Hide the Identities of Agents
In a leaked memo, an ICE official tells employees the new AI tech will protect them. Some worry it could be used to root out whistleblowers.
Why the Algorithm Loves a Villain, And How to Beat It
When the internet is full of distortions, fake news, and AI-generated slop, how can facts and journalism rise to the top? Former BBC and Vice journalist Sophia Smith Galer has one possible way to…
Deepfake video of Zelenskyy could be 'tip of the iceberg' in info war, experts warn
A fake video of the Ukrainian president claiming defeat spread on social media on Wednesday.
Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study
Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.
Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study
Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.
ICE agents are making house calls for online critics
DHS keeps accusing people of ‘doxing’ its agents/
W Social, Fictional Metrics and the Beauty of Open Data
W Social's tagline is "Trust Your Feed" but the company's landing page displays inflated engagement metrics - a misrepresentation that contradicts its own promise.
Media Influence Matrix – The World's Most Reliable Influence Tracker
This week’s edition reads five FY2025 reports against each other: Dnevnik (Slovenia), Delfi Latvia, Phoenix New Media (China), Hanza Media (Croatia) and Digi Communications (Romania). It finds two publishers separating their journalism from their balance sheets, a Croatian publisher closing a 35-year-old political weekly on a 1.1% net margin, a Chinese state-linked digital news group whose financial centre has quietly shifted from advertising to mini-program reading apps, and a Romanian telecom giant’s having its news channel walk off the must-carry list.