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This symposium reports on a study done in Ghana to understand how participation in authentic water and sanitation research affects students’ learning of science practices, their attitudes toward science, their interest in science-related careers, and their identities as scientists. It also sought effective ways to scaffold science teachers’ engagement of their students in authentic science activities. It was found students increased their knowledge of science and their ability to engage in science practices; they increased their interest in STEM-related careers; and they took on identities as scientists. Teachers gained new content and pedagogical methods to incorporate in their teaching. 

This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus (Davies and Fuchs, 2015), and are human-annotated as containing either a euphemistic or literal usage of a PET. In this paper, we present the results and analyze the common themes, methods and findings of the participating teams. 

Abstract Drylands are critical in regulating global carbon sequestration, but the resiliency of these semi‐arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical‐Infrared (IR) and microwave remote sensing observations to quantify plant‐to‐stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. Machine‐learning was employed to re‐construct global satellite microwave vegetation optical depth (VOD) retrievals to 500‐m resolution. The re‐constructed results were able to delineate diverse vegetation conditions undetectable from the original 25‐km VOD record, and showed overall favorable correspondence with in situ plant water potential measurements (R from 0.60 to 0.78). The VOD water potential estimates effectively tracked plant water storage changes from hydro‐climate variability over diverse sub‐regions. The re‐constructed VOD record improves satellite capabilities for monitoring the storage and movement of water across the soil‐vegetation‐atmosphere continuum in heterogeneous drylands. 

Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nevertheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT). 

Hundreds of millions of people worldwide have limited access to safe, clean drinking water. Although for most Americans this problem may seem very far removed from their experience, there are many resources available on the internet that can bring the reality of water scarcity into the classroom. We have found this to be a problem that resonates with many students when they become aware of how it affects people their own age. Experimenting with BSFs is a way for students to participate in solving the problem of water scarcity, poor water quality, and inadequate sanitation that have negatively impacted the health and livelihoods for families around the world. In addition, it can provide students with a voice and empower their capacity in STEM in two ways, first by their authentic engagement in the SEPs, and second, by investigating ways to enhance the efficacy and operation of BSFs that could help those in need of an inexpensive way to purify their water 

Proceedings of the Fourth Workshop on Natural Language Processing for Internet Freedom (NLP4IF) Workshop: Censorship, Disinformation, and Propaganda 

We explore linguistic features that contribute to sarcasm detection. The linguistic features that we investigate are a combination of text and word complexity, stylistic and psychological features. We experiment with sarcastic tweets with and without context. The results of our experiments indicate that contextual information is crucial for sarcasm prediction. One important observation is that sarcastic tweets are typically incongruent with their context in terms of sentiment or emotional load. 

Abstract: The lack of readily available sources of potable water is major problem in many parts of the world. This project engaged high school (HS) students in authentic and meaningful science and engineering activities to teach them about the lack and poor quality of potable water in many regions and how they can be addressed through the use of point of use (POU) treatments, such as biosand filters (BSFs). The HS students’ activities paralleled those of USF students, including research question development and BSF design, construction, operation, and monitoring. An ethnographic approach was utilized by incorporating participant observation, collection and review of artifacts, and interviews. It was found that the project’s focus on the need to provide potable water in the developing world provided authenticity and meaningfulness to the HS students, which encouraged their participation in activities and the learning of science and engineering practices. The HS students reported an awareness of the differences between this project and their regular science classes. The project had a positive impact on their perceptions of themselves as scientists and their interest in STEM careers. The HS students’ results were useful to the university-based research. In addition, the USF students gained teaching experience while investigating research questions in a low-stakes environment. 

We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leaderboards for the tasks are available at h t t p : //gitlab. com/NLP4I F/nlp4i f- 2021. 

This paper studies how the linguistic components of blogposts collected from Sina Weibo, a Chinese microblogging platform, might affect the blogposts’ likelihood of being censored. Our results go along with King et al. (2013)’s Collective Action Potential (CAP) theory, which states that a blogpost’s potential of causing riot or assembly in real life is the key determinant of it getting censored. Although there is not a definitive measure of this construct, the linguistic features that we identify as discriminatory go along with the CAP theory. We build a classifier that significantly outperforms non-expert humans in predicting whether a blogpost will be censored. The crowdsourcing results suggest that while humans tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterparts, they in general cannot make a better guess than our model when it comes to ‘reading the mind’ of the censors in deciding whether a blogpost should be censored. We do not claim that censorship is only determined by the linguistic features. There are many other factors contributing to censorship decisions. The focus of the present paper is on the linguistic form of blogposts. Our work suggests that it is possible to use linguistic properties of social media posts to automatically predict if they are going to be censored.