TAG-it@ EVALITA 2020: Overview of the Topic, Age, and Gender Prediction Task for Italian

The Topic, Age, and Gender (TAG-it) prediction task in Italian was organised in the context of EVALITA 2020, using forum posts as textual evidence for profiling their authors. The task was articulated in two separate subtasks: one where all three dimensions (topic, gender, age) were to be predicted at once; the other where training and test sets were drawn from different forum topics and gender or age had to be predicted separately. Teams tackled the problems both with classical machine learning methods as well as neural models. Using the training-data to fine-tuning a BERT-based monolingual model for Italian proved eventually as the most successful strategy in both subtasks. We observe that topic and gender are easier to predict than age. The higher results for gender obtained in this shared task with respect to a comparable challenge at EVALITA 2018 might be due to the larger evidence per author provided at this edition, as well as to the availability of pre-trained large models for fine-tuning, which have shown improvement on very many NLP tasks

Recensione di V. Bochicchio, Percezione

Book-revie

Supervisors behaving badly: Witnessing Ethical Dilemmas and What to Do About It

The NASW Code of Ethics (1996) guides social workers’ professional conduct, but provides little instruction when one’s own supervisor behaves unethically. Using student-collected interviews, we found six typologies of supervisors behaving badly, and used descriptive qualitative analysis to outline steps taken to navigate the situation. Results hold pedagogical relevance to social work practice

A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages

This work focuses on the analysis of Italian social media messages for disaster management and aims at the detection of messages carrying critical information for the damage assessment task. A main novelty of this study consists in the focus on out-domain and cross-event damage detection, and on the investigation of the most relevant tweet-derived features for these tasks. We devised different experiments by resorting to a wide set of linguistic features qualifying the lexical and grammatical structure of a text as well as ad-hoc features specifically implemented for this task. We investigated the most effective features that allow to achieve the best results. A further result of this study is the construction of the first manually annotated Italian corpus of social media messages for damage assessment

Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media

[EN] In recent years, social media have become ubiquitous and important for social networking and content sharing. Moreover, the content generated by these websites remains largely untapped. Some researchers proved that social media have been a valuable source to predict the future outcomes of some events such as box-office movie revenues or political elections. Social media are also used by companies to measure the sentiment of customers about their brand and products. This work proposes a new social media based model to measure how users perceive new products from a technical point of view. This model relies on the analysis of advantages and drawbacks of products, which are both important aspects evaluated by consumers during the buying decision process. This model is based on a lexicon developed in a related work (Chiarello et. al, 2017) to analyse patents and detect advantages and drawbacks connected to a certain technology. The results show that when a product has a certain technological complexity and fuels a more technical debate, advantages and drawbacks analysis is more efficient than sentiment analysis in producing technical-functional judgements.Chiarello, F.; Bonaccorsi, A.; Fantoni, G.; Ossola, G.; Cimino, A.; Dell'orletta, F. (2018). Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 145-156. https://doi.org/10.4995/CARMA2018.2018.8336OCS14515

Crisis Mapping during Natural Disasters via Text Analysis of Social Media Messages

Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by analyzing the textual content of disaster reports from a twofold perspective. A damage detection component employs a SVM classifier to detect mentions of damage among emergency reports. A novel geoparsing technique is proposed and used to perform message geolocation. We report on a case study to show how the information extracted through damage detection and message geolocation can be combined to produce accurate crisis maps. Our crisis maps clearly detect both highly and lightly damaged areas, thus opening up the possibility to prioritize rescue efforts where they are most needed

Addiction & Trauma Considerations for Women in Reentry

Research shows that more than 92°/o of our individuals who complete the residential program stay out of the criminal justice system after one year. As part of Alvis Residential programs, a comprehensive range of evidence-based programs and services are delivered to help individuals who are transitioning from the corrections system back into the community. Alvis provides these services in a safe, supportive environment, so that individuals receive the guidance they need to navigate the challenges of finding employment and reconnecting with family members.https://fuse.franklin.edu/ss2016/1055/thumbnail.jp