Room to Grow: Understanding Personal Characteristics Behind Self
Improvement Using Social Media
- URL: http://arxiv.org/abs/2105.08031v1
- Date: Mon, 17 May 2021 17:30:30 GMT
- Title: Room to Grow: Understanding Personal Characteristics Behind Self
Improvement Using Social Media
- Authors: MeiXing Dong, Xueming Xu, Yiwei Zhang, Ian Stewart, Rada Mihalcea
- Abstract summary: We study the motivation-related behavior of people who persist with their intention to change.
Our experiments provide new insights into the motivation-related behavior of people who persist with their intention to change.
- Score: 27.699640898659283
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many people aim for change, but not everyone succeeds. While there are a
number of social psychology theories that propose motivation-related
characteristics of those who persist with change, few computational studies
have explored the motivational stage of personal change. In this paper, we
investigate a new dataset consisting of the writings of people who manifest
intention to change, some of whom persist while others do not. Using a variety
of linguistic analysis techniques, we first examine the writing patterns that
distinguish the two groups of people. Persistent people tend to reference more
topics related to long-term self-improvement and use a more complicated writing
style. Drawing on these consistent differences, we build a classifier that can
reliably identify the people more likely to persist, based on their language.
Our experiments provide new insights into the motivation-related behavior of
people who persist with their intention to change.
Related papers
- 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) - Ink and Individuality: Crafting a Personalised Narrative in the Age of LLMs [0.0]
Growing reliance on LLM-based writing assistants risks compromising our creativity and individuality over time.
This study investigates these concerns by performing a brief survey to explore different perspectives and concepts.
Addressing these issues is essential for improving human-computer interaction systems and enhancing writing assistants for personalization and individuality.
arXiv Detail & Related papers (2024-03-20T21:02:16Z) - PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for
Personality Detection [50.66968526809069]
We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner.
Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
arXiv Detail & Related papers (2023-10-31T08:23:33Z) - 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) - Editing Personality for Large Language Models [73.59001811199823]
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs)
We construct a new benchmark dataset PersonalityEdit to address this task.
arXiv Detail & Related papers (2023-10-03T16:02:36Z) - Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech
Emotion Recognition [48.29355616574199]
We analyze the transferability of emotion recognition across three different languages--English, Mandarin Chinese, and Cantonese.
This study concludes that different language and age groups require specific speech features, thus making cross-lingual inference an unsuitable method.
arXiv Detail & Related papers (2023-06-26T08:48:08Z) - Understanding How People Rate Their Conversations [73.17730062864314]
We conduct a study to better understand how people rate their interactions with conversational agents.
We focus on agreeableness and extraversion as variables that may explain variation in ratings.
arXiv Detail & Related papers (2022-06-01T00:45:32Z) - Identifying Moments of Change from Longitudinal User Text [16.45577617206016]
We define a new task of identifying moments of change in individuals on the basis of their shared content online.
The changes we consider are sudden shifts in mood (switches) or gradual mood progression (escalations)
We have created detailed guidelines for capturing moments of change and a corpus of 500 manually annotated user timelines.
arXiv Detail & Related papers (2022-05-11T16:03:47Z) - Mental Disorders on Online Social Media Through the Lens of Language and
Behaviour: Analysis and Visualisation [7.133136338850781]
We study the factors that characterise and differentiate social media users affected by mental disorders.
Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary.
We find evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder.
arXiv Detail & Related papers (2022-02-07T15:29:01Z) - Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers
Reveals Distinctive yet Consistent Individual Styles [7.4037154707453965]
We introduce a new approach to studying idiolects through a massive cross-author comparison to identify and encode stylistic features.
A neural model achieves strong performance at authorship identification on short texts.
We quantify the relative contributions of different linguistic elements to idiolectal variation.
arXiv Detail & Related papers (2021-09-07T15:49:23Z) - Revealing Persona Biases in Dialogue Systems [64.96908171646808]
We present the first large-scale study on persona biases in dialogue systems.
We conduct analyses on personas of different social classes, sexual orientations, races, and genders.
In our studies of the Blender and DialoGPT dialogue systems, we show that the choice of personas can affect the degree of harms in generated responses.
arXiv Detail & Related papers (2021-04-18T05:44:41Z)
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.