Depression and Self-Harm Risk Assessment in Online Forums

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4, FastText baseline, and CNN-

Bots as Virtual Confederates: Design and Ethics

The use of bots as virtual confederates in online field experiments holds extreme promise as a new methodological tool in computational social science. However, this potential tool comes with inherent ethical challenges. Informed consent can be difficult to obtain in many cases, and the use of confederates necessarily implies the use of deception. In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation. We draw upon examples from prior work in the CSCW community and the broader social science literature for illustration. While a handful of prior researchers have used bots in online experimentation, our work is meant to inspire future work in this area and raise awareness of the associated ethical issues.Comment: Forthcoming in CSCW 201

TA-COS 2016 : First workshop on text analytics for cybersecurity and online safety : Proceedings

B

Privacy-preserving design of graph neural networks with applications to vertical federated learning

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM). The surging developments of graph representation learning (GRL) have opened up new opportunities for FRM applications under FL via efficiently utilizing the graph-structured data generated from underlying transaction networks. Meanwhile, transaction information is often considered highly sensitive. To prevent data leakage during training, it is critical to develop FL protocols with formal privacy guarantees. In this paper, we present an end-to-end GRL framework in the VFL setting called VESPER, which is built upon a general privatization scheme termed perturbed message passing (PMP) that allows the privatization of many popular graph neural architectures.Based on PMP, we discuss the strengths and weaknesses of specific design choices of concrete graph neural architectures and provide solutions and improvements for both dense and sparse graphs. Extensive empirical evaluations over both public datasets and an industry dataset demonstrate that VESPER is capable of training high-performance GNN models over both sparse and dense graphs under reasonable privacy budgets

Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements

As generative large model capabilities advance, safety concerns become more pronounced in their outputs. To ensure the sustainable growth of the AI ecosystem, it's imperative to undertake a holistic evaluation and refinement of associated safety risks. This survey presents a framework for safety research pertaining to large models, delineating the landscape of safety risks as well as safety evaluation and improvement methods. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models, encompassing preference-based testing, adversarial attack approaches, issues detection, and other advanced evaluation methods. Additionally, we explore the strategies for enhancing large model safety from training to deployment, highlighting cutting-edge safety approaches for each stage in building large models. Finally, we discuss the core challenges in advancing towards more responsible AI, including the interpretability of safety mechanisms, ongoing safety issues, and robustness against malicious attacks. Through this survey, we aim to provide clear technical guidance for safety researchers and encourage further study on the safety of large models

Nowcasting user behaviour with social media and smart devices on a longitudinal basis: from macro- to micro-level modelling

The adoption of social media and smart devices by millions of users worldwide over the last decade has resulted in an unprecedented opportunity for NLP and social sciences. Users publish their thoughts and opinions on everyday issues through social media platforms, while they record their digital traces through their smart devices. Mining these rich resources offers new opportunities in sensing real-world events and indices (e.g., political preference, mental health indices) in a longitudinal fashion, either at the macro (population)-, or at the micro(user)-level. The current project aims at developing approaches to “nowcast" (predict the current state of) such indices at both levels of granularity. First, we build natural language resources for the static tasks of sentiment analysis, emotion disclosure and sarcasm detection over user-generated content. These are important for opinion monitoring on a large scale. Second, we propose a general approach that leverages textual data derived from generic social media streams to nowcast political indices at the macro-level. Third, we leverage temporally sensitive and asynchronous information to nowcast the political stance of social media users, at the micro-level using multiple kernel learning. We then focus further on the micro-level modelling, to account for heterogeneous data sources, such as information derived from users' smart phones, SMS and social media messages, to nowcast time-varying mental health indices of a small cohort of users on a longitudinal basis. Finally, we present the challenges faced when applying such micro-level approaches in a real-world setting and propose directions for future research

Empathy Detection Using Machine Learning on Text, Audiovisual, Audio or Physiological Signals

Empathy is a social skill that indicates an individual's ability to understand others. Over the past few years, empathy has drawn attention from various disciplines, including but not limited to Affective Computing, Cognitive Science and Psychology. Empathy is a context-dependent term; thus, detecting or recognising empathy has potential applications in society, healthcare and education. Despite being a broad and overlapping topic, the avenue of empathy detection studies leveraging Machine Learning remains underexplored from a holistic literature perspective. To this end, we systematically collect and screen 801 papers from 10 well-known databases and analyse the selected 54 papers. We group the papers based on input modalities of empathy detection systems, i.e., text, audiovisual, audio and physiological signals. We examine modality-specific pre-processing and network architecture design protocols, popular dataset descriptions and availability details, and evaluation protocols. We further discuss the potential applications, deployment challenges and research gaps in the Affective Computing-based empathy domain, which can facilitate new avenues of exploration. We believe that our work is a stepping stone to developing a privacy-preserving and unbiased empathic system inclusive of culture, diversity and multilingualism that can be deployed in practice to enhance the overall well-being of human life

Graphs behind data: A network-based approach to model different scenarios

openAl giorno d’oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e più nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialità di affrontare con successo molti problemi aperti in diversi contesti. ​Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. ​INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc

How to Create an Innovation Accelerator

Too many policy failures are fundamentally failures of knowledge. This has become particularly apparent during the recent financial and economic crisis, which is questioning the validity of mainstream scholarly paradigms. We propose to pursue a multi-disciplinary approach and to establish new institutional settings which remove or reduce obstacles impeding efficient knowledge creation. We provided suggestions on (i) how to modernize and improve the academic publication system, and (ii) how to support scientific coordination, communication, and co-creation in large-scale multi-disciplinary projects. Both constitute important elements of what we envision to be a novel ICT infrastructure called "Innovation Accelerator" or "Knowledge Accelerator".Comment: 32 pages, Visioneer White Paper, see http://www.visioneer.ethz.c