Predicting Personality on Social Media with Semi-supervised Learning

Nie, Dong; Guan, Zengda; Hao, Bibo; Bai, Shuotian; Zhu, Tingshao

doi:10.1109/wi-iat.2014.93

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…Other works, e.g. Nie et al (2014), give no indication concerning the amount of text being used per individual. These questions are especially relevant given that social media communications are likely highly variable, exhibiting fluctuations arising from a variety of factors, such as mood (Bradley and Mogg, 1994).…”

Section: Discussionmentioning

confidence: 99%

Individual Differences in Strategic Deception

Appling

Briscoe

2016

Proceedings of the Second Workshop on Computational Approaches To Deception Detection

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While text-based deception in computer mediated communication has been studied, e.g., Zhou (2005) and Duran et al. (2010), there has been less focus on the differentiation of strategies for deception, especially those which may manifest in modern communication, such as found in social media. In this paper, we extend our previous work on the evaluation of linguistic indicators to strategic deception (Appling et al., 2015), by evaluating the relationship between personality and deceptive strategy use and the utilization of linguistic features for inferring both (personality and deception). We find that even with a relatively small corpus, there is evidence that personality is related to particular deception strategies, though in short social media communications, these personality traits are difficult to infer using standard linguistic measures (e.g., LIWC). We also describe the corpus we collected from an experiment in which subjects engaged in deception through a social media platform.

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Section: Discussionmentioning

confidence: 99%

Individual Differences in Strategic Deception

Appling

Briscoe

2016

Proceedings of the Second Workshop on Computational Approaches To Deception Detection

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“…It also contributes to a much broader spectrum and intensity to society. The article suggests that cybercrimes are preventable by analyzing people's behavioral changes and sentiment analysis by predicting their thoughts through their social media posts using iterative clustering [16]; [17]. This approach can be crucial in providing solutions to socio-emotional problems or carrying out criminal activities.…”

Section: Related Workmentioning

confidence: 99%

Prediction of The Security Threats in Social Media Using Artificial Intelligence

Saeed

Mohsen

2021

QZJ

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highlights that further gotten extremely significant in deciding a person's weakness to social designing dangers and assaults. The point is to; fundamentally study the conventional viewpoint and never-ending viewpoint responses towards socially designed dangers day by day. This exploration utilizes a near examination of the speculations and the essential information discoveries to show that specific practices of Facebook clients are a danger to different clients. The outcomes have demonstrated how AI calculations work in distinguishing misleading messages and con artists through AI methods .

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“…The proposed method is language independent, does not require particular lexicons, and has been successfully applied to different languages [34]. To develop this method, the 1000 most frequent words labeled with Big Five personality traits and Myers-Briggs personality types were Several studies [75,107] have described improperly labeled samples in published research and have developed semi-supervised learning methods to evaluate the personality traits by using unlabeled samples. Nie et al [75] have extracted 47 features for each user in the categories of the user personal profile, social circles, social activities, and social habits.…”

Section: Predicting Human Behaviormentioning

confidence: 99%

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

et al. 2020

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The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data. In this group, the data-based approaches had three main components: (1) collecting self-reported survey data, (2) collecting data from social media and extracting data features, and (3) applying correlation analysis to evaluate the relationship between two sets of data. In contrast, the second group focused on the accuracy of data-based approaches for predicting human behavior. In this group, the data-based approaches could be categorized into (1) approaches based on labeled unstructured textual data and (2) approaches based on unlabeled unstructured textual data. The review provides a comprehensive insight into unstructured textual data mining to identify and predict human behavior and personality traits.

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Predicting Personality on Social Media with Semi-supervised Learning

Cited by 22 publications

References 10 publications

Individual Differences in Strategic Deception

Individual Differences in Strategic Deception

Prediction of The Security Threats in Social Media Using Artificial Intelligence

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

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