Social World Knowledge: Modeling and Applications
- URL: http://arxiv.org/abs/2306.16299v1
- Date: Wed, 28 Jun 2023 15:25:30 GMT
- Title: Social World Knowledge: Modeling and Applications
- Authors: Nir Lotan and Einat Minkov
- Abstract summary: Social world knowledge is a key ingredient in effective communication and information processing by humans and machines alike.
We introduce SocialVec, a framework for eliciting low-dimensional entity embeddings from the social contexts in which they occur in social networks.
Similar to word embeddings which facilitate tasks that involve text semantics, we expect the learned social entity embeddings to benefit multiple tasks of social flavor.
- Score: 2.9417848476446364
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Social world knowledge is a key ingredient in effective communication and
information processing by humans and machines alike. As of today, there exist
many knowledge bases that represent factual world knowledge. Yet, there is no
resource that is designed to capture social aspects of world knowledge. We
believe that this work makes an important step towards the formulation and
construction of such a resource. We introduce SocialVec, a general framework
for eliciting low-dimensional entity embeddings from the social contexts in
which they occur in social networks. In this framework, entities correspond to
highly popular accounts which invoke general interest. We assume that entities
that individual users tend to co-follow are socially related, and use this
definition of social context to learn the entity embeddings. Similar to word
embeddings which facilitate tasks that involve text semantics, we expect the
learned social entity embeddings to benefit multiple tasks of social flavor. In
this work, we elicited the social embeddings of roughly 200K entities from a
sample of 1.3M Twitter users and the accounts that they follow. We employ and
gauge the resulting embeddings on two tasks of social importance. First, we
assess the political bias of news sources in terms of entity similarity in the
social embedding space. Second, we predict the personal traits of individual
Twitter users based on the social embeddings of entities that they follow. In
both cases, we show advantageous or competitive performance using our approach
compared with task-specific baselines. We further show that existing entity
embedding schemes, which are fact-based, fail to capture social aspects of
knowledge. We make the learned social entity embeddings available to the
research community to support further exploration of social world knowledge and
its applications.
Related papers
- From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition [59.57095498284501]
We propose a novel approach that recognizes textbfContextual textbfSocial textbfRelationships (textbfConSoR) from a social cognitive perspective.
We construct social-aware descriptive language prompts with social relationships for each image.
Impressively, ConSoR outperforms previous methods with a 12.2% gain on the People-in-Social-Context (PISC) dataset and a 9.8% increase on the People-in-Photo-Album (PIPA) benchmark.
arXiv Detail & Related papers (2024-06-12T16:02:28Z) - Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future [59.78608958395464]
We build a Social AI Data Infrastructure, which consists of a comprehensive social AI taxonomy and a data library of 480 NLP datasets.
Our infrastructure allows us to analyze existing dataset efforts, and also evaluate language models' performance in different social intelligence aspects.
We show there is a need for multifaceted datasets, increased diversity in language and culture, more long-tailed social situations, and more interactive data in future social intelligence data efforts.
arXiv Detail & Related papers (2024-02-28T00:22:42Z) - SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans.
In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals.
We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
arXiv Detail & Related papers (2023-10-18T02:27:01Z) - SocialVec: Social Entity Embeddings [1.4010916616909745]
This paper introduces SocialVec, a framework for eliciting social world knowledge from social networks.
We learn social embeddings for roughly 200,000 popular accounts from a sample of the Twitter network.
We exploit SocialVec embeddings for gauging the political bias of news sources in Twitter.
arXiv Detail & Related papers (2021-11-05T14:13:01Z) - SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement
Learning Agents [23.719833581321033]
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI.
We argue that aiming towards human-level AI requires a broader set of key social skills.
We present SocialAI, a benchmark to assess the acquisition of social skills of DRL agents.
arXiv Detail & Related papers (2021-07-02T10:39:18Z) - Social Behaviour Understanding using Deep Neural Networks: Development
of Social Intelligence Systems [2.107969466194361]
Social computing has evolved beyond social informatics toward the birth of social intelligence systems.
This paper takes initiatives to propose a social behaviour understanding framework with the use of deep neural networks for social and behavioural analysis.
Three systems, including depression detection, activity recognition and cognitive impairment screening, are developed to evidently demonstrate the importance of social intelligence.
arXiv Detail & Related papers (2021-05-20T03:19:55Z) - PHASE: PHysically-grounded Abstract Social Events for Machine Social
Perception [50.551003004553806]
We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions.
Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events.
As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
arXiv Detail & Related papers (2021-03-02T18:44:57Z) - Can You be More Social? Injecting Politeness and Positivity into
Task-Oriented Conversational Agents [60.27066549589362]
Social language used by human agents is associated with greater users' responsiveness and task completion.
The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element.
Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
arXiv Detail & Related papers (2020-12-29T08:22:48Z) - Graph-Based Social Relation Reasoning [101.9402771161935]
We propose a graph relational reasoning network (GR2N) for social relation recognition.
Our method considers the paradigm of jointly inferring the relations by constructing a social relation graph.
Experimental results illustrate that our method generates a reasonable and consistent social relation graph.
arXiv Detail & Related papers (2020-07-15T03:01:11Z) - Mathematical Foundations for Social Computing [21.041093050431183]
Social computing encompasses the mechanisms through which people interact with computational systems.
In June 2015, we brought together roughly 25 experts in related fields to discuss the promise and challenges of establishing mathematical foundations for social computing.
This document captures several of the key ideas discussed.
arXiv Detail & Related papers (2020-07-07T17:50:27Z) - Towards Social Identity in Socio-Cognitive Agents [0.0]
We propose a socio-cognitive agent model based on the concept of Cognitive Social Frames.
Cognitive Social Frames can be built around social groups, and form the basis for social group dynamics mechanisms and construct of Social Identity.
arXiv Detail & Related papers (2020-01-20T15:27:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.