scholarly journals Applying machine learning algorithms for deriving personality traits in social network

2018 ◽  
Author(s):  
Eric F. M. Araújo ◽  
Bojan Simoski ◽  
Michel Klein
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Personality Traits ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals The Efficiency of Social Network Services Management in Organizations. An In-Depth Analysis Applying Machine Learning Algorithms and Multiple Linear Regressions

2020 ◽  
Vol 10 (15) ◽  
pp. 5167 ◽  
Author(s):  
Luis Matosas-López ◽  
Alberto Romero-Ania
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Gender Equality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Network Services ◽  
Content Increase ◽  
Multiple Linear Regressions ◽  
Social Network Services ◽  
Linear Regressions

The objective of this work is to detect the variables that allow organizations to manage their social network services efficiently. The study, applying machine learning algorithms and multiple linear regressions, reveals which aspects of published content increase the recognition of publications through retweets and favorites. The authors examine (I) the characteristics of the content (publication volumes, publication components, and publication moments) and (II) the message of the content (publication topics). The research considers 21,771 publications and thirty-nine variables. The results show that the recognition obtained through retweets and favorites is conditioned both by the characteristics of the content and by the message of the content. The recognition through retweets improves when the organization uses links, hashtags, and topics related to gender equality, whereas the recognition through favorites increases when the organization uses original tweets, publications between 8:00 and 10:00 a.m. and, again, gender equality related topics. The findings of this research provide new knowledge about trends and patterns of use in social media, providing academics and professionals with the necessary guidelines to efficiently manage these technologies in the organizational field.

scholarly journals Developing and evaluating language-based machine learning algorithms for inferring applicant personality in video interviews

2021 ◽  
Author(s):  
Louis Hickman ◽  
Rachel Saef ◽  
Vincent Ng ◽  
Sang Eun Woo ◽  
Louis Tay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Personality Traits ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Big Five Personality ◽  
Test Machine ◽  
Self Reports ◽  
The Cross

Organizations are increasingly relying on people analytics to aid human resources decision-making. One application involves using machine learning to automatically infer applicant characteristics from employment interview responses. However, management research has provided scant validity evidence to guide organizations’ decisions about whether and how best to implement these algorithmic approaches. To address this gap, we use closed vocabulary text mining on mock video interviews to train and test machine learning algorithms for predicting interviewee’s self-reported (automatic personality recognition) and interviewer-rated personality traits (automatic personality perception). We use 10-fold cross-validation to test the algorithms’ accuracy for predicting Big Five personality traits across both rating sources. The cross-validated accuracy for predicting self-reports was lower than large-scale investigations using language in social media posts as predictors. The cross-validated accuracy for predicting interviewer ratings of personality was more than double that found for predicting self-reports. We discuss implications for future research and practice.

scholarly journals Examination of Assorted Social Engineering Attack by Different Types of Machine Learning Algorithms

2021 ◽  
Vol 10 (7) ◽  
pp. 56-60
Author(s):  
Amal Alhamad ◽  
Dalal Aldablan ◽  
Raghad Albahlal
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Social Engineering ◽  
Machine Learning Algorithms ◽  
Privacy And Security ◽  
Different Types ◽  
Tree Algorithms

The most powerful attack on the systems is Social Engineering Attack because of this attack deals with Psychology so that there is no hardware or software can prevent it or even can defend it and hence people need to be trained to defend against it.[1] Social engineering is mostly done by phone or email. In this research, which is based on previous research we have conducted, the aim of it was of it was to highlight the different social engineering attacks and how they can prevent in social network because social engineering is one of the biggest problems in social network, a concern the privacy and security. This project is using a set of data then analysis it uses the Weka tool, to defend against these attacks we have evaluated three decision tree algorithms, RandomForest, REPTree and RandomTree. It was also related to an J48 algorithm, On the contrary, here contains a complete overview of social engineering attacks, also more than one algorithm was searched.

Basic principles of TikTok algorithms

2021 ◽  
Vol 5 (2(15)) ◽  
pp. 61-76
Author(s):  
Vasilii Konstantinovich Alekhin ◽  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Learning Algorithms ◽  
User Interaction ◽  
Machine Learning Algorithms ◽  
Behavioral Patterns ◽  
Basic Principles ◽  
The Difference ◽  
Audio Track ◽  
Database Clustering

Social network TikTok has strong competitive differentiator in comparing with other platforms. ByteDance exploits machine learning algorithms to generate a recommendation feed (for you page). The algorithm bases on two main mechanisms. The first mechanism provides content database clustering depending on the type, audio track, video captions, and hashtags. The second mechanism analyzes the user’s behavioral patterns based on their actions in the application. The next step is the formation of user interaction scenarios. The difference between the predicted behavior and the real one is the object of analysis. If it equals zero, then the recommendations feed is formed correctly. The user is watching more and more interesting videos, just scrolling through video after video.

scholarly journals Adversarial Machine Learning on Social Network: A Survey

2021 ◽  
Vol 9 ◽  
Author(s):  
Sensen Guo ◽  
Xiaoyu Li ◽  
Zhiying Mu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Research Progress ◽  
Future Research

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

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