Dataset on individual differences in self-reported personality and inferred emotional expression in profile pictures of Italian Facebook users

We retrieved the current profile picture of 2234 Italian Facebook users who also answered self-report questionnaires on demographic variables and personality. Data were collected between March and June 2018 using a Facebook wep application. Profile pictures consisting of 200x200 resolution jpegs were obtained by sending a request via the Facebook Graph API and analyzed using online commercial services allowing for the scoring of facial expressions in image data, namely Microsoft Azure Face APIand MEGVII Face++ Detect API. Both services provide emotional expression scores if at least one (n = 1) face is successfully detected in the picture. Using the Microsoft Azure Face API we obtained scores for anger, contempt, disgust, fear, joy, sadness, surprise, and neutrality. Using the MEGVII Face++ API, pictures were scored for the presence of anger, disgust, fear, joy, sadness, and surprise, and neutrality. Higher scores on each emotion refer to a stronger expression of the respective emotion. The dataset presented here consists of data of N =728 Facebook users with a profile picture in which both APIs detected only one (N=1) face. Regarding self-report data, the dataset includes the following demographic information about the participants: gender and age. The dataset also includes participants’ personality scores based on a short validated assessment of Big Five traits (Ten Item Personality Inventory), and Impulsivity/Sensation Seeking (IMPSS8). A document included the questions administered in the online survey is attached to the dataset. This dataset can be useful to generate insights on the association between demographic variables, including age and gender, and personality (Big Five traits and Impulsivity/Sensation Seeking), and emotional expression as derived from social media pictures. It can be useful for researchers and data scientists who do research in social sciences, in particular psychoinformatics, to train models in order to infer personality of users of social media platforms from profile pictures. The annexed files include the following: DIB_DATASET_25_10_2021.csv (the actual data) DIB_DATASET_Codebook.xlsx (the codebook for the data) Supplementary material - Online survey.docx (doc file including questions administered to participants)本研究检索了2234名意大利脸书（Facebook）用户的当前社交头像，这些用户同时填写了涵盖人口统计学变量与人格特质的自陈问卷。 数据采集工作于2018年3月至6月间通过脸书网页应用完成。 研究通过脸书图形应用程序接口（Facebook Graph API）发送请求，获取了分辨率为200×200像素的JPEG格式头像；随后借助两款可对图像数据中的面部表情进行评分的在线商业服务完成分析，分别为微软Azure面部识别API（Microsoft Azure Face API）与旷视Face++检测API（MEGVII Face++ Detect API）。 若头像中成功检测到至少1张人脸（n=1），两款服务均可输出情绪表达评分。 借助微软Azure面部识别API，我们获取了愤怒、轻蔑、厌恶、恐惧、快乐、悲伤、惊讶与中性共8类情绪的评分。 而通过旷视Face++检测API，研究为头像标注了愤怒、厌恶、恐惧、快乐、悲伤、惊讶与中性共7类情绪的评分。 各项情绪的评分越高，代表对应情绪的表达强度越强。 本数据集最终纳入728名脸书用户的有效数据，这些用户的头像均被两款API成功检测且仅包含1张人脸（N=1）。 在自陈调研数据部分，本数据集包含参与者的两项人口统计学信息：性别与年龄。 数据集同时包含基于经过信效度验证的简版大五人格特质（Big Five traits）测评量表——十项人格量表（Ten Item Personality Inventory）以及冲动性/感觉寻求量表（Impulsivity/Sensation Seeking, IMPSS8）计算得到的参与者人格特质评分。 本数据集附带一份包含本次线上调查问卷全部题目的文档。 本数据集可用于探究人口统计学变量（含年龄与性别）、人格特质（大五人格与冲动性/感觉寻求）与社交媒体头像所提取的情绪表达之间的关联，为相关研究提供实证洞见。 同时，本数据集可助力社会科学（尤其是心理信息学（psychoinformatics））领域的研究人员与数据科学家训练模型，以通过社交媒体用户的社交头像推断其人格特质。 所附带的附属文件包括：DIB_DATASET_25_10_2021.csv（原始数据集文件）、DIB_DATASET_Codebook.xlsx（数据集编码手册）以及补充材料——Online survey.docx（包含本次线上调查问卷全部题目的Word文档）。