It's possible to predict some violent public protests by tracking social media posts on moral outrage over a triggering event. Tracy Le Blanc/Pexels

Every grade school teacher knows that student conduct can get out of hand, fast, when a group of kids eggs on one individual. Time-outs are a testimony to the power of isolating one 10-year-old from a choir of buddies.

Social media plays a role similar to a gang of hyped-up grade schoolers, providing a community that can express collective disapproval of people or events. When this disapproval has a moral cast ⁠— for example, after a police shooting or the removal of a statue ⁠— the social network's particular characteristics are key predictors about whether that disapproval will turn violent.

There is a word for the way group support of a belief system makes it seem worth fighting for: moralization. Tracking social network activity now makes it possible to measure the chances for an individual belief to become moralized by a group ⁠— a phenomenon known as moral convergence.

In a recent study in Nature, Rice Business professor Marlon Mooijman, then at the Kellogg School of Management, joined a team that analyzed when and how violence erupts in protests. In a series of observation and behavior experiments that mixed psychology, organizational theory and computer science, they accurately predicted how violence is influenced by group discussion of moral views on social media.

The researchers started by studying the number and content of tweets linked to the Baltimore riots in 2015, after the death of Freddie Gray in police custody. The researchers then compared these tweets with the number of arrests in a given time frame, using a methodology developed by Marlon Mooijman and Joe Hoover from the Brain and Creativity Institute at the University of Southern California.

To analyze the tweets responding to Gray's death, they first separated them into two sets: Those with moral commentary and those without moral judgments.

Next, the researchers tracked whether tweets with moral content increased on days with violent protests. Violence was measured using the number of police arrests, which the researchers compared with the specific time frames of moral tweets.

There was no major difference in the overall tweet traffic discussing Freddie Gray's death on days with violent protests and on peaceful days. The number of moralizing tweets, however, clearly correlated with episodes of violent protests, rising to nearly double the moralizing tweets on days with no violence.

This raised a provocative question. Were morally ⁠— based tweets a response to the events of the day ⁠— or were they somehow driving the violence?

To find out, Mooijman and Hoover worked with computer scientists Ying Lin and Jeng Ji of Rensselaer Polytechnic Institute and Morteza Dehghani of the University of Southern California to develop algorithms that could establish mathematical probabilities for the results.

For every single-unit increase in moral tweets over a 4-hour period, the researchers found, there was a .25 corresponding increase in arrests.

The researchers then tried to measure the effect similar moral views ⁠— such as a social media page with self-selected members of a similar political affiliation ⁠— had on violence during protests.

To do so, they set up a second study, which measured participant reactions to the protestors of a far-right rally in Charlottesville, Virginia in 2017. Participants ranked their level of agreement over the morality of protesting the rally.

There was a direct relationship between believing a protest action was moral, the researchers found, and finding violence at that protest acceptable. This relationship held true throughout the study, regardless of political orientation.

The researchers' next goal was to identify the impact of exposure to people of like beliefs. To do this, participants rated their feelings when they were told that most people in the U.S. shared their views. While the intensity of participants' moral views created the potential for violence, the researchers found, violence resulted when only actively validated by others with similar views.

Having one's moral outrage supported by others on social media, the professors concluded, may explain the spike in violence in recent protests.

While respect for privacy remains critical, governments and law enforcement can use the social media trend to pinpoint the moments when moral outrage can turn deadly. Perhaps most importantly, however, the research also suggests practical tactics for calming violent tendencies before they get out of control. To reduce real-life protest violence, they wrote, it's critical that social media sites include a variety of voices. It's another reason, if any were needed, that a bit of judicious exposure to other views is healthy for everyone.

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This story originally ran on Rice Business Wisdom.

Marlon Mooijman is an assistant professor of Organizational Behavior. He teaches in the undergraduate business minor program and MBA full-time program.

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Rice, Houston Methodist award $90K for cancer research projects

seed grants

Rice University’s Synthesis X Center, in partnership with the Houston Methodist Neal Cancer Center, announced earlier this month the organizations would award $90,000 in seed grant funding to two projects that could help fight cancer. One would ease the pain of chemotherapy, while the other could change the way the progression of leukemia is tracked.

“We’re excited to be collaborating with the Neal Cancer Center to support collaborative, novel and interdisciplinary proposals to improve cancer care and outcomes,” Han Xiao, Rice professor of chemistry and the director of the SynthX Center, said in a news release. “Together, we can achieve translational excellence.”

The projects

Chemotherapy is one of the most effective cancer treatments, but it can be hard on the body. The human body doesn’t like being injected with radioactive material, especially the skin around the injection point, which can become extremely irritated. For patients in long-term treatment, the skin irritation can be more than just a bother; it can lead to infections that are dangerous to a compromised immune system.

Angel A Marti, a professor of chemistry at Rice University, and Biana Godin, an associate professor of nanoscience at Houston Methodist Research Institute, are experimenting with metal nanoclusters as a way to block radiation at the injection site. The nanocluster could be applied in a cream or a gel on the skin, serving as a type of shield against the harsh radioactive material.

Meanwhile, Yuan Ma, an assistant professor of chemistry at Rice, and Shu-Hsia Chen, a professor of immunology at Houston Methodist Research Institute, are working with m6A. Discovered in the 1970s, m6A is the most prevalent chemical modifier found in mRNA in mammals. It is prevalent in many cancers, including leukemia.

Ma and Chen are working on measuring the amount of m6A in leukemia to see if it can determine the most effective cancer treatments. The team is also experimenting with ways to shut off m6A to see if it makes current leukemia treatments more effective.

Progress from SynthX

SynthX was first launched in April 2024 to turn research from Rice and Houston Methodist into real-world cancer treatments. Within a year, the center had secured $1.5 million in grant money to work on crossing the blood-brain barrier in brain cancer treatments. These latest awards show that SynthX Center continues to bridge the worlds of research and clinical practice.

“This collaboration reflects a shared commitment to team science in cancer research,” Daniela Matei, the director of the Houston Methodist Neal Cancer Center, added in the release. “These are the types of translational and pioneering projects that lead to transformation in patient care.”'

The SynthX Center awarded $90,000 in seed grants to three teams in 2025 and $80,000 to three teams in 2024.

Tesla self-driving mode wasn't to blame in Houston-area crash, report suggests

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Federal safety investigators looking into a runaway Tesla that killed a grandmother in her home say the driver had pressed the accelerator to full speed, suggesting the vehicle's self-driving software was not to blame.

The driver had told police that he had the self-driving software turned on, but a report from the National Transportation Safety Board concluded that he had actually overridden that feature when he pushed hard on the pedal. Moments later the Tesla Model 3 raced down a residential street in Katy, Texas, at highway speeds, slammed into a brick home and killed a 76-year-old woman standing in the front room.

The crash last month drew national attention because Tesla CEO Elon Musk is seeking to reassure the public its self-driving feature is safe as he prepares to turn hundreds of thousands of Teslas already on the road into fully automatic vehicles and begin selling two-seated Cybercabs missing steering wheels and pedals.

The crash came two months after officials at a separate federal agency, the National Highway Traffic Safety Administration, announced it was elevating a 2024 investigation of the self-driving feature to new “engineering analysis” level, raising the possibility of a recall of 3.2 million Tesla vehicles.

That NHTSA probe was triggered by crashes where the self-driving feature failed to alert drivers to take control in fog and other poor visibility conditions.

The agency opened an investigation last year into 58 incidents in which Teslas reportedly violated traffic safety laws while using self-driving technology, leading to more than a dozen crashes and fires and nearly two dozen injuries.

Separate from the National Transportation Safety Board, NHTSA is also looking into the Tesla house crash in Texas, one of 46 “special crash” investigations of Tesla's self-driving or driver-assistance technology in the past decade, according to the agency’s records. In more than a dozen of those crashes, at least one person — a driver, passenger or pedestrian — was killed.

Tesla had originally called its driver assistance software Full Self-Driving, or FSD, but auto experts and regulators complained it was misleading because drivers must always keep their eyes on the road and be ready to take over at any time.

The company has since changed the name to Full Self-Driving (Supervised).

Video of the Katy, Texas, accident shows the Tesla traveling at more than 70 mph (112.65 kilometers per hour), jumping a curb then tearing across a lawn before crushing through a brick wall of a home. A woman standing feet away, Martha Avila, was found amid piles of crumbling plaster, split beams and bits of furniture and rushed to a hospital but died.

Sales of Tesla cars still haven't recovered fully from boycotts last year over Musk's political stands, but the stock is rising anyway as he has successfully shifted attention away from the sales figures. He says they matter less now that the company is on the cusp of major technological advances, such as turning Teslas into hands-free vehicles and having its Optimus robots take over for humans for tasks at home and work.

Tesla stock has risen 22% in the past year and is currently trading at 170 times expected annual earnings compared to 20 for the S&P 500.

For its second-quarter financial results, financial analysts surveyed by FactSet expect earnings per share will barely budge — 32 cents versus 33 cents a year earlier — continuing a sixth quarter streak of flat or falling profits.

London AI startup selects Houston for first U.S. office after $20M raise

welcome to houston

London-based AI firm Applied Computing has announced a $20 million Series A round and a new office in Houston.

The new Bayou City office is Applied Computing’s first in the United States and part of its North American expansion. The company is known for its Orbital AI platform, which is tailored for energy operations.

The funding round was led by Houston-based KBR Inc., with participation from San Francisco-based Databricks Ventures. KBR’s investment was first announced in March.

KBR and Applied Computing have also entered into a multi-year agreement to deliver exclusive AI products for the energy sector. KBR already has integrated Orbital into its INSITE 3.0 platform for energy projects, and is also using the product for ammonia production.

Applied Computing’s Orbital platform combines physics-grounded intelligence with models across chemical engineering, time-series forecasting and language, according to the company. The system analyzes sensor readings and can recognize a facility’s equipment constraints and operator activity. The platform can also allow technicians to run simulations of how a change to a facility could affect the rest of its operations.

According to TechCrunch, Applied Computing will use the $20 million to further explore projects and deployments with the energy sector, hire engineering and research positions, and continue to expand internationally, potentially into the Middle East.

The company is also working on deals with a major U.S. stream operator, TechCrunch reports. And Applied Computing shared on LinkedIn that it plans to announce its first partnership with a major European oil company in the coming weeks.

“Yesterday we showed Orbital live in deployments at our demo day at the Energy Institute in London,” Callum Adamson, CEO and co-founder of Applied Computing, posted on LinkedIn on July 16. “Today, we're announcing the capital to scale it globally as well as the launch of our new offices in Houston and Bangalore. In the weeks following, there will be more announcements on our progress, partnerships and deployments.”

The company opened its Bangalore offices in December.