On Predicting Personal Values of Social Media Users using
Community-Specific Language Features and Personal Value Correlation
- URL: http://arxiv.org/abs/2007.08107v1
- Date: Thu, 16 Jul 2020 04:36:13 GMT
- Title: On Predicting Personal Values of Social Media Users using
Community-Specific Language Features and Personal Value Correlation
- Authors: Amila Silva, Pei-Chi Lo, Ee-Peng Lim
- Abstract summary: This work focuses on analyzing Singapore users' personal values and developing effective models to predict their personal values using their Facebook data.
We incorporate the correlations among personal values into our proposed Stack Model consisting of a task-specific layer of base models and a cross-stitch layer model.
- Score: 14.12186042953335
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Personal values have significant influence on individuals' behaviors,
preferences, and decision making. It is therefore not a surprise that personal
values of a person could influence his or her social media content and
activities. Instead of getting users to complete personal value questionnaire,
researchers have looked into a non-intrusive and highly scalable approach to
predict personal values using user-generated social media data. Nevertheless,
geographical differences in word usage and profile information are issues to be
addressed when designing such prediction models. In this work, we focus on
analyzing Singapore users' personal values, and developing effective models to
predict their personal values using their Facebook data. These models leverage
on word categories in Linguistic Inquiry and Word Count (LIWC) and correlations
among personal values. The LIWC word categories are adapted to non-English word
use in Singapore. We incorporate the correlations among personal values into
our proposed Stack Model consisting of a task-specific layer of base models and
a cross-stitch layer model. Through experiments, we show that our proposed
model predicts personal values with considerable improvement of accuracy over
the previous works. Moreover, we use the stack model to predict the personal
values of a large community of Twitter users using their public tweet content
and empirically derive several interesting findings about their online behavior
consistent with earlier findings in the social science and social media
literature.
Related papers
- ComPO: Community Preferences for Language Model Personalization [122.54846260663922]
ComPO is a method to personalize preference optimization in language models.
We collect and release ComPRed, a question answering dataset with community-level preferences from Reddit.
arXiv Detail & Related papers (2024-10-21T14:02:40Z) - Identifying Privacy Personas [27.301741710016223]
Privacy personas capture the differences in user segments with respect to one's knowledge, behavioural patterns, level of self-efficacy, and perception of the importance of privacy protection.
While various privacy personas have been derived in the literature, they group together people who differ from each other in terms of important attributes.
We propose eight personas that we derive by combining qualitative and quantitative analysis of the responses to an interactive educational questionnaire.
arXiv Detail & Related papers (2024-10-17T20:49:46Z) - Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis [1.2903829793534267]
This study, explores the correlation between the use of Arabic language on twitter, personality traits and its impact on sentiment analysis.
We indicated the personality traits of users based on the information extracted from their profile activities, and the content of their tweets.
Our findings demonstrated that personality affect sentiment in social media.
arXiv Detail & Related papers (2024-07-08T18:27:54Z) - Detecting value-expressive text posts in Russian social media [0.0]
We aimed to find a model that can accurately detect value-expressive posts in Russian social media VKontakte.
A training dataset of 5,035 posts was annotated by three experts, 304 crowd-workers and ChatGPT.
ChatGPT was more consistent but struggled with spam detection.
arXiv Detail & Related papers (2023-12-14T14:18:27Z) - Decoding the Silent Majority: Inducing Belief Augmented Social Graph
with Large Language Model for Response Forecasting [74.68371461260946]
SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics.
Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
arXiv Detail & Related papers (2023-10-20T06:17:02Z) - Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona
Biases in Dialogue Systems [103.416202777731]
We study "persona biases", which we define to be the sensitivity of dialogue models' harmful behaviors contingent upon the personas they adopt.
We categorize persona biases into biases in harmful expression and harmful agreement, and establish a comprehensive evaluation framework to measure persona biases in five aspects: Offensiveness, Toxic Continuation, Regard, Stereotype Agreement, and Toxic Agreement.
arXiv Detail & Related papers (2023-10-08T21:03:18Z) - Measuring the Effect of Influential Messages on Varying Personas [67.1149173905004]
We present a new task, Response Forecasting on Personas for News Media, to estimate the response a persona might have upon seeing a news message.
The proposed task not only introduces personalization in the modeling but also predicts the sentiment polarity and intensity of each response.
This enables more accurate and comprehensive inference on the mental state of the persona.
arXiv Detail & Related papers (2023-05-25T21:01:00Z) - Unifying Data Perspectivism and Personalization: An Application to
Social Norms [10.480094567764606]
We examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms.
We apply personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms.
arXiv Detail & Related papers (2022-10-26T07:43:26Z) - 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) - Predicting Relationship Labels and Individual Personality Traits from
Telecommunication History in Social Networks using Hawkes Processes [5.668126716715423]
Mobile phones contain a wealth of private information, so we try to keep them secure.
We provide large-scale evidence that the psychological profiles of individuals and their relations with their peers can be predicted from seemingly anonymous communication traces.
arXiv Detail & Related papers (2020-09-04T07:24:49Z) - Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset
for Personality Assessment [50.15466026089435]
We present a novel peer-to-peer Hindi conversation dataset- Vyaktitv.
It consists of high-quality audio and video recordings of the participants, with Hinglish textual transcriptions for each conversation.
The dataset also contains a rich set of socio-demographic features, like income, cultural orientation, amongst several others, for all the participants.
arXiv Detail & Related papers (2020-08-31T17:44:28Z)
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.