基于微博行为的用户情感需求识别研究
文献类型：学位论文
| 作者 | 邓荭 |
| 答辩日期 | 2023-06 |
| 文献子类 | 继续教育硕士 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 赵楠 |
| 关键词 | 情感需求 网络行为 机器学习 |
| 学位名称 | 理学硕士 |
| 学位专业 | 应用心理学 |
| 其他题名 | Research on users' need for affect recognition based on their Weibo behavior |
| 中文摘要 | The Need for Affect (NFA) refers to the motivation to approach or avoid emotion-inducing situations and encompasses three dimensions: NFA total, NFA approach, and NFA avoidance. As a relatively stable intrinsic aspect of human nature, NFA plays a crucial role in mental health, information processing, decision-making, social attitudes, and various other social psychological processes and behaviors. However, traditional data collection and research methods, such as self-reporting, are not suitable for large-scale assessment scenarios. Consequently, finding an efficient and automatic method for identifying NFA on a large scale is both theoretically and practically significant. This study explores the relationship between users' social network behavior, particularly on Weibo, and their NFA, aiming to automate NFA identification based on social network data. The study comprises three aspects: Study 1 examines the relationship between users' NFA and Weibo behaviors, investigating the correlation between specific Weibo behaviors and users' NFA. The results indicate that users with a high level of NFA approach are more focused on themselves, use richer language expression patterns, like to express their true feelings and emotions, are more likely to seek sensory experiences and stimulation, and are more willing to interact with others. Users with a high level of NFA avoidance are more concerned about others, like to interact with familiar people, and use more death-related vocabulary in their Weibo posts. Users with high NFA total levels tend to express their emotions and interact with others, and use death-related vocabulary less frequently. The results reveal a close relationship between Weibo behavior and NFA, demonstrating the feasibility of NFA identification based on user behavior on Weibo. Study 2 predicts users' NFA scores based on their Weibo behaviors, using regression algorithms to model and predict these scores from selected Weibo features. The results show that Extreme Gradient Boosting (XGB) performs best among the eight machine learning algorithms used. The Pearson correlation coefficients between predicted scores and NFA questionnaire scores achieved 0.25 (NFA avoidance), 0.31 (NFA approach) and 0.34 (NFA total), and the split-half reliabilities were 0.66 (NFA total), 0.68 (NFA approach) and 0.70 (NFA avoidance). Study 3 classifies users' NFA levels based on Weibo behavior, using selected features and classification algorithms to model and identify users' NFA levels. The results indicate that Random Forest (RF) has the best classification effect on NFA total and NFA approach. The precision rates of high and low group classification of NFA total are 0.68 and 0.64 respectively, while the precision rates of high and low group classification of NFA approach are 0.65 and 0.64 respectively. Logistic Regression (LG) performs best on NFA avoidance, with precision rates of high and low group classification being 0.63 and 0.62 respectively. Overall, this study demonstrates a correlation between users' Weibo behavior and their NFA, suggesting the feasibility of identifying users' NFA through Weibo behavior. The proposed non-invasive method for NFA identification can be applied to mental health monitoring and other large-scale NFA measurement scenarios, complementing traditional scale measurement methods. |
| 英文摘要 | 情感需求是人们趋近或回避情感体验的动机，包括总情感需求、情感趋近和情感回避三个维度，它是人们追求情绪体验的相对稳定的个体差异，是影响人们情绪体验和表达的一个重要因素。情感需求在解释和理解心理健康，信息处理与决策制定，社会态度以及很多社会心理学相关领域的心理过程和行为上发挥着重要的作用。由于数据采集和研究方法的局限性，以往研究中使用自我报告的方式测量情感需求的方法难以完全满足大规模群体监测的场景。如何快速便捷地大规模自动识别情感需求具有理论和实际意义。本研究试图探讨用户的社交网络行为与其情感需求之间的关系，并进一步基于社交网络数据实现对用户情感需求的自动化识别。具体包括以下三项研究: 研究一，用户情感需求与微博行为的关系研究，探讨新浪微博用户的特定行为和语言表达与其情感需求的相关性。结果表明，在微博环境中情感趋近水平高的用户更加关注自我，使用更加丰富的语言表达模式，更加喜欢表达自己的真实感受和情感，更可能寻求感官方面的体验和刺激，并且更加愿意与他人互动。情感回避水平高的用户更加关注他人，喜欢和熟悉的人互动，且在微博文本中使用更多死亡相关的词汇。总情感需求高的用户更加喜欢表达情感、与他人互动，且使用死亡相关词汇的频率更低。该研究结果验证了微博行为与情感需求之间具有密切关联，也进一步说明基于微博行为识别情感需求的可行性。 研究二，基于微博行为的情感需求得分预测，基于选择的微博特征，采用回归算法建模预测用户的情感需求得分。结果表明，使用的8种机器学习算法中，XGB算法对总情感需求、情感趋近和情感回避的预测效果最佳，其中预测值与真实值的相关系数即模型的效度分别达到0.34(总情感需求)、0.31(情感趋近)和0.25(情感回避)，分半信度分别为0.66(总情感需求)、0.68(情感趋近)和0.70(情感回避)。 研究三，基于微博行为的情感需求水平分类识别，基于情感需求高低分组的微博特征，采用分类算法建模对用户情感需求水平的高低进行分类。结果表明，在6种分类算法中，随机森林对于总情感需求和情感趋近的分类效果最好，其中，总情感需求高低分组分类的精确率分别为0.68和0.64，情感趋近高低分组分类的精确率分别为0.65和0.64，逻辑回归对于情感回避的分类效果最好，高分组和低分组的精确率分别为0.63和0.62 0 本研究系统考察和揭示了用户的社交网络行为与其情感需求之间的关系，验证了利用微博行为识别用户情感需求的可行性，并通过心理学研究方法结合机器学习算法构建预测模型，提出了一种无侵扰式识别用户情感需求的方法。该识别方法可用于心理健康监测和其他需要大规模测量情感需求的场景，与传统的量表测量法互为补充。 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/45149]  |
| 专题 | 心理研究所_社会与工程心理学研究室 |
| 推荐引用方式 GB/T 7714 | 邓荭. 基于微博行为的用户情感需求识别研究[D]. 中国科学院心理研究所. 中国科学院大学. 2023. |
入库方式： OAI收割
来源：心理研究所
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