scholarly journals Career guidance based on machine learning: social networks in professional identity construction

2020 ◽  
Vol 169 ◽  
pp. 158-163
Author(s):  
Pavel Kiselev ◽  
Boris Kiselev ◽  
Valeriya Matsuta ◽  
Artem Feshchenko ◽  
Irina Bogdanovskaya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Professional Identity ◽  
Identity Construction ◽  
Career Guidance

scholarly journals Professional identity and experience of undergraduate students: an analysis of semantic networks

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Luara Carvalho ◽  
Elisa Maria Barbosa de Amorim-Ribeiro ◽  
Marcelo do Vale Cunha ◽  
Luciana Mourão
Keyword(s):  
Professional Identity ◽  
Identity Construction ◽  
Undergraduate Students ◽  
Semantic Networks ◽  
Work Experiences ◽  
Undergraduate Studies ◽  
Course Work

AbstractWork experiences during undergraduate studies can be remarkable in the journey of undergraduate students. The objective of this study was to assess, by analyzing semantic networks, the role of work experiences in the meanings those individuals attribute to professional identity. The sample consisted of 2291 students (60% women) divided into three groups: do not work, work in a field related to their course, work in a field not related to their course. The semantic networks of these groups were composed of words uttered from the professional identity prime. We chose to work with the critical network, obtained from the analysis of the incidence-fidelity indexes of the word pairs. The results evidence that work experiences are related to how undergraduate students attribute meaning to professional identity, in such a way that three different networks were formed for these groups. The network of those who work outside their field was the only one that integrated words with negative content, while the semantic networks of those who do not work and those who work in their field, despite containing words that do not always coincide, present a similar macrostructure. We conclude that work experiences play an important role in the meanings that undergraduate students attribute to professional identity. The study innovates by revealing elements of professional-identity construction, besides allowing for reflections on the effects of work experiences during the college period.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

scholarly journals Assessing pre-service teachers’ professional identity construction in a three-phase teacher education program in Iran

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Fariba Haghighi Irani ◽  
Azizeh Chalak ◽  
Hossein Heidari Tabrizi
Keyword(s):  
Teacher Education ◽  
Professional Identity ◽  
Identity Construction ◽  
Education Program ◽  
Teacher Identity ◽  
Teacher Educators ◽  
Teacher Education Program ◽  
Critical Role ◽  
Assessment Tools ◽  
Problem Solving Skills

Abstract The critical role of teachers suggests that assessing teacher identity construction helps teacher educators understand the changes in teachers and design materials in harmony with their needs in teacher education programs. However, only a few studies have focused on assessing pre-service teachers’ identity in the long term in Iran. To address this gap, the contribution of a pre-service teacher education program consisting of three phases, namely engage, study, and activate to the professional identity construction of eight pre-service teachers in an institute in Tehran was assessed. Pre-course and post-course interviews, two reflective essays, ten observation notes, and two teaching performances were gathered over a year and analyzed as guided by grounded theory and discourse analysis. Findings revealed two significant changes in the participants’ identities when they transitioned from engage to study and from study to activate phases that yielded study phase as the peak of the changes. Overall, three major shifts were identified in the participants’ identities: from a commitment to evaluation towards a commitment to modality, from one-dimensional to multi-dimensional perceptions, and from problem analysis to problem-solving skills. Current findings may facilitate teacher identity construction by designing local programs matching the needs of pre-service teachers. It may also assist teacher educators by assessing the quality of teachers’ performance and developing teacher assessment tools.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Exploratory Study ◽  
Representation Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Twitter Users ◽  
Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

scholarly journals A machine-learning based approach to privacy-aware information-sharing in mobile social networks

2016 ◽  
Vol 25 ◽  
pp. 125-142 ◽  
Author(s):  
Igor Bilogrevic ◽  
Kévin Huguenin ◽  
Berker Agir ◽  
Murtuza Jadliwala ◽  
Maria Gazaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Information Sharing ◽  
Mobile Social Networks

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

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