Simone Stolzoff: 'My intolerance of uncertainty was causing so much angst.' Simone Stolzoff: 'My intolerance of uncertainty was causing so much angst.' S imone Stolzoff describes himself as "naturally an uncertain person" inclined to rumination and self-doubt. This tendency benefits him in his work as a journalist, but can otherwise be a double-edged sword. While working for a magazine in New York, Stolzoff was approached about a job at a design firm in San Francisco.

The rise of social media has been argued to intensify uncivil and hostile online political discourse. Yet, to date, there is a lack of clarity on what incivility means in the political sphere. In this work, we utilize a multidimensional perspective of political incivility, developed in the fields of political science and communication, that differentiates between impoliteness and political intolerance. We present state-of-the-art incivility detection results using a large dataset of 13K political tweets, collected and annotated per this distinction. Applying political incivility detection at large-scale, we observe that political incivility demonstrates a highly skewed distribution over users, and examine social factors that correlate with incivility at subpopulation and user-level. Finally, we propose an approach for modeling social context information about the tweet author alongside the tweet content, showing that this leads to improved performance on the task of political incivility detection. We believe that this latter result holds promise for socially-informed text processing in general.

Due to the severity of the social media offensive and hateful comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale expert annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection. The HateBR corpus was collected from the comment section of Brazilian politicians' accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. The corpus consists of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level classification (highly, moderately, and slightly offensive), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). We also implemented baseline experiments for offensive language and hate speech detection and compared them with a literature baseline. Results show that the baseline experiments on our corpus outperform the current state-of-the-art for the Portuguese language.

Over the past decades, breakthroughs such as Reinforcement Learning (RL) and Agent-based modeling (ABM) have made simulations of economic models feasible. Recently, there has been increasing interest in applying ABM to study the impact of residential preferences on neighborhood segregation in the Schelling Segregation Model. In this paper, RL is combined with ABM to simulate a modified Schelling Segregation model, which incorporates moving expenses as an input parameter. In particular, deep Q network (DQN) is adopted as RL agents' learning algorithm to simulate the behaviors of households and their preferences. This paper studies the impact of moving expenses on the overall segregation pattern and its role in social integration. A more comprehensive simulation of the segregation model is built for policymakers to forecast the potential consequences of their policies.

Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for annotating offensive online speech. Detecting offensive content is rapidly becoming one of the most important real-world NLP tasks. However, most datasets use a single binary label, e.g., for hate or incivility, even though each concept is multi-faceted. This modeling choice severely limits nuanced insights, but also performance. We show that a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content addresses both conceptual and performance issues. We release a novel dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance. Our dataset not only allows for a more nuanced understanding of harmful speech online, models trained on it also outperform or match performance on benchmark datasets.

A predictive model of a polarized group, similar to the current U.S. Senate, demonstrates that when an outside threat – like war or a pandemic – fails to unite the group, the divide may be irreversible through democratic means. Published today in the Proceedings of the National Academy of Sciences as part of a Dynamics of Political Polarization Special Feature, the model identifies such atypical behavior among the political elite as a powerful symptom of dangerously high levels of polarization. "We see this very disturbing pattern in which a shock brings people a little bit closer initially, but if polarization is too extreme, eventually the effects of a shared fate are swamped by the existing divisions and people become divided even on the shock issue," said network scientist Boleslaw Szymanski, a professor of computer science and director of the Army Research Laboratory Network Science and Technology Center (NeST) at Rensselaer Polytechnic Institute. "If we reach that point, we cannot unite even in the face of war, climate change, pandemics, or other challenges to the survival of our society." The model – essentially a game that simulates the views of 100 theoretical legislators over time – allowed researchers to dial up party identity, intolerance for disagreement, and extremism to levels such that almost no degree of shock could unite the legislative group. In some situations, the simulation revealed that even the strongest shock fails to reverse the self-reinforcing dynamics of political polarization.

The craving for Artificial intelligence is escalating day by day. Latterly, the obsession for AI-enabled tech is gaining a lot of attention. With an increase in the magnetism of the consumers towards AI, companies are battling hard to value the development in AI. The main issue is the difficulty in grasping the concept of Artificial Intelligence. The capability of AI and it's working style needs to be understood before trying AI for your business. I will be covering some trends of AI in this write-up with the relevance to audiences who are aiming to explore artificial intelligence and its potential.

No one knows what the future holds, but many people are unable to cope with the uncertainty. However, researchers have discovered that the fear of the unknown may be linked to an unusual enlargement of a brain region that is responsible for decision making and motor control. The team believes the findings could help specialists predict those at risk of developing anxiety disorder or OCD later in life, allowing intervention to occur before symptoms arise. Researchers at Dartmouth College conducted MRI scans on 61 students following a survey that measured their ability to tolerate the uncertainty of future negative events. The team analyzed the scans and compared them with the intolerance of uncertainty scores, which showed the size of the striatum was linked with intolerance of uncertainty.

On March 23rd, 2016, Microsoft released a chunk of artificial intelligence onto the Internet. Dubbed "Tay," this was a bot1 designed to chat with real human beings, simulating a 19-year-old female, learning from those humans how to act more human. On March 24th, less than 24 hours later, Microsoft put Tay to sleep. She was spewing neo-Nazi, xenophobic, racist tweets. Apparently, Tay had been learning from the wrong humans--those who had chosen to teach her.

On March 23rd, 2016, Microsoft released a chunk of artificial intelligence onto the Internet. Dubbed "Tay," this was a bot1 designed to chat with real human beings, simulating a 19-year-old female, learning from those humans how to act more human. On March 24th, less than 24 hours later, Microsoft put Tay to sleep. She was spewing neo-Nazi, xenophobic, racist tweets. Apparently, Tay had been learning from the wrong humans--those who had chosen to teach her.