Inside Meta's Efforts to Prevent School Shootings Algorithmic social media, which has helped create the conditions for the spread of such violence, is reckoning with how it can be part of the solution. Larry Baimbridge spent thirty-one years with the Houston Police Department, including stints as a homicide detective and as commander of the team. In 2023, he took a job as chief of police for the Spring Branch Independent School District, most of which is within the city. The job was more low-key--no more shootouts with snipers--but it came with its own challenges. After the 2022 shooting in Uvalde, Texas, which left nineteen elementary-school students and two teachers dead, the state legislature had neglected to pass any meaningful restrictions on guns; instead, the state required that every school campus have an armed security officer.

Inside Meta's Efforts to Prevent School Shootings Algorithmic social media, which has helped create the conditions for the spread of such violence, is reckoning with how it can be part of the solution. Larry Baimbridge spent thirty-one years with the Houston Police Department, including stints as a homicide detective and as commander of the team. In 2023, he took a job as chief of police for the Spring Branch Independent School District, most of which is within the city. The job was more low-key--no more shootouts with snipers--but it came with its own challenges. After the 2022 shooting in Uvalde, Texas, which left nineteen elementary-school students and two teachers dead, the state legislature had neglected to pass any meaningful restrictions on guns; instead, the state required that every school campus have an armed security officer.

Steve Cargil's family has farmed the flat, dry land around Uvalde, a small city in southwest Texas, since 1953. Cargil, 67, had always assumed that one of his three children would eventually take it over, the way he had from his father. But that assumption took a hit one night in 2018 after a violent hailstorm. Cargil and his son, Ryan, had spent months nursing crops of cabbage and onions to harvest. Then a storm rolled through and destroyed them overnight.

Two summers ago, the Santa Monica-based company behind the popular video game "Call of Duty" sent a letter to a 24-year-old man in Antioch, Tenn., who went by the online handle "Lerggy." Known in real life as Ryan Rothholz, court filings say, he is the creator of "Lergware," hacking software that enabled Call of Duty players to cheat by kicking opponents offline. A lawsuit filed in May against Rothholz and others allegedly involved in the hacking scheme is the latest salvo in years-long campaign by Activision-Blizzard and other companies to rid their games of cheating. The war is being waged in the Central District of California civil courts, but the defendants are scattered across the country and as far away as Australia. An immersive "first-person shooter" game, Call of Duty takes players into simulated, realistic military combat.

Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events affect pavement deterioration, specifically focusing on measuring pavement roughness by the International Roughness Index (IRI). To quantify these effects, we utilized 20 years of pavement condition data from TxDOT's PMIS database, which is integrated with flood event data, including duration and spatial extent. Statistical analyses were performed to compare IRI values before and after flooding and to calculate the deterioration rates influenced by flood exposure. Moreover, we applied Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to assess the impact of flooding on pavement performance. The results demonstrate that flood-affected pavements experience a more rapid increase in roughness compared to non-flooded sections. These findings emphasize the need for proactive flood mitigation strategies, including improved drainage systems, flood-resistant materials, and preventative maintenance, to enhance pavement resilience in vulnerable regions.

In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. We did all of our experiments for Hindi-English code-mixed scenario, as we have the linguistic expertise for the same. We experimented with three approaches, namely (i) native sample mixing, (ii) multi-task learning (MTL), and (iii) prompting very large multilingual language models (VMLMs). In native sample mixing, we added monolingual task samples in code-mixed training sets. In MTL learning, we relied on native and code-mixed samples of a semantically related task (hate detection in our case). Finally, in our third approach, we evaluated the efficacy of VMLMs via few-shot context prompting. Some interesting findings we got are (i) adding native samples improved humor (raising the F1-score up to 6.76%) and sarcasm (raising the F1-score up to 8.64%) detection, (ii) training MLMs in an MTL framework boosted performance for both humour (raising the F1-score up to 10.67%) and sarcasm (increment up to 12.35% in F1-score) detection, and (iii) prompting VMLMs couldn't outperform the other approaches. Finally, our ablation studies and error analysis discovered the cases where our model is yet to improve. We provided our code for reproducibility.

The high volume and rapid evolution of content on social media present major challenges for studying the stance of social media users. In this work, we develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the user-user interaction graph. In the first stage, a simple and efficient heuristic for stance labeling uses the user-hashtag bipartite graph to iteratively update the stance association of user and hashtag nodes via a label propagation mechanism. This set of soft labels is then integrated with the user-user interaction graph to train a graph neural network (GNN) model using semi-supervised learning. We evaluate this method on two large-scale datasets containing tweets related to climate change from June 2021 to June 2022 and gun control from January 2022 to January 2023. Our experiments demonstrate that enriching text-based embeddings of users with network information from the user interaction graph using our semi-supervised GNN method outperforms both classifiers trained on user textual embeddings and zero-shot classification using LLMs such as GPT4. We discuss the need for integrating nuanced understanding from social science with the scalability of computational methods to better understand how polarization on social media occurs for divisive issues such as climate change and gun control.

At a Fresno County elementary school, a masked man with a fake gun carried out an active-shooter drill without most of the teachers and parents being informed ahead of time. At San Marino High School, police officers planned to fire blanks to mimic the sound of gunfire, but the drill was ultimately canceled over concerns of traumatizing students. More recently, a principal at a San Gabriel elementary school was placed on a leave of absence after allegedly using her fingers to mime holding a gun and pretending to shoot kids, telling them, "Boom. The rise in active-shooter drills at American schools has coincided with the growing phenomenon of mass shootings in the U.S., as well as media coverage focused on school massacres including Columbine, Sandy Hook and Uvalde. These drills have taken place at 95% of U.S. public schools as of the 2015-16 school year, according to the Education Department's National Center for Education statistics.

Families of gun violence victims are using artificial intelligence to recreate their loved ones' voices and taunt lawmakers who oppose gun control on the sixth anniversary of the Parkland massacre. The robocall messages are being sent to senators and House members who support the National Rifle Association and Second Amendment rights in a campaign that launched on Valentine's Day, Wednesday, according to the Associated Press. Manuel and Patricia Oliver, whose son Joaquin "Guac" Oliver died in the 2018 high school shooting in Parkland, Florida, said the campaign run through The Shotline website is intended to spur Congress to ban the sale of guns like the AR-15 rifle. "We come from a place where gun violence is a problem, but you will never see a 19-year-old with an AR-15 getting into a school and shooting people," Manuel Oliver told the Associated Press in an interview. The Olivers, immigrants from Venezuela, became activists after Joaquin and 13 other students at Marjory Stoneman Douglas High School were murdered by a 19-year-old killer with a rifle.

Six years ago today, Joaquin Oliver was killed in a hallway outside his Florida classroom, one of 17 students and staff murdered in the worst high school shooting in the US. On Wednesday, lawmakers in Washington DC will hear his voice, recreated by artificial intelligence, in phone calls demanding to know why they've done nothing to tackle the plague of gun violence. "It's been six years and you've done nothing. Not a thing to stop all the shootings that have happened since," the message from Oliver, who was 17 when he died in the 2018 Valentine's Day's tragedy at Parkland's Marjory Stoneman Douglas high school, says. "I'm back today because my parents used AI to recreate my voice to call you. Other victims like me will be calling too, again and again, to demand action. How many calls will it take for you to care? How many dead voices will you hear before you finally listen?"