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Does the social media Platform Change the Impression of Teaching Assistants?: A Facebook vs. Twitter Study

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social Networking 2025年第1期14卷 1-13页

作者： Zuoming Wang Department of Communication Studies University of North Texas Denton Texas USA

This study analyzed how impressions are formed online depending on the type of social media and the implications that may come from the over-disclosure of information. Using a Qualtrics survey, 97 participants viewed the profile of a female teaching assistant on Twitter and Facebook. While there was little difference between the two social media, the use of self-disclosure on Twitter seemed slightly more inappropriate for sharing personal information.

关键词： Impression Online social media Teaching Assistant

social media in Git Hub: the role of @-mention in assisting software development

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Science China(Information Sciences) 2017年第3期60卷 50-67页

作者： Yang ZHANG Huaimin WANG Gang YIN Tao WANG Yue YU Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology

Recently, many researches propose that social media tools can promote the collaboration among developers, which are beneficial to the software development. Nevertheless, there is little empirical evidence to confirm that using @-mention has indeed a beneficial impact on the issues in Git Hub. In order to begin investigating such claim, we examine data from two large and successful projects hosted on Git Hub, the Ruby on Rails and the Angular JS. By using qualitative and quantitative analysis, we give an in-depth understanding on how @-mention is used in the issues and the role of @-mention in assisting software development. Our statistical results indicate that, @-mention attracts more participants and tends to be used in the difficult issues. @-mention favors the solving process of issues by enlarging the visibility of issues and facilitating the developers' collaboration. Our study also build an @-network based on the @-mention database we *** the @-network, we investigate its evolution over time and prove that we certainly have the potential to mine the relationships and characteristics of developers by exploiting the knowledge from the @-network.

关键词： issues social media @-mention Git Hub software development

social media as passive geo-participation in transportation planning-how effective are topic modeling&sentiment analysis in comparison with citizen surveys?

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Geo-Spatial Information Science 2020年第4期23卷 275-292页

作者： Oliver Lock Christopher Pettit City Futures Research Centre Faculty of Built EnvironmentUniversity of New South Wales(UNSW)SydneyAustralia Expanded Perception and Interaction Centre(Epicentre) Faculty of Art&DesignUniversity of New South Wales(UNSW)SydneyAustralia

We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and *** transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the *** such pressures on existing public transportation systems,this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such *** research forms a case study of the use of passively collected forms of big data in cities-focusing on Sydney,***,it examines social media data(Tweets)related to public transport ***,it joins this to longitudinal big data-delay information continuously broadcast by the network over a year,thus forming hundreds of millions of data ***,tones,and sentiment are modeled using machine learning and Natural Language Processing(NLP)*** resulting data,and models,are compared to opinions derived from a citizen survey among *** validity of such data and models versus the intentions of users,in the context of systems that monitor and improve transport performance,are *** such,key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques.

关键词： social media smart cities public participation urban sensing transport planning natural language processing machine learning big data

social media and mobility landscape:Uncovering spatial patterns of urban human mobility with multi source data

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Frontiers of Environmental Science & Engineering 2018年第5期12卷 101-114页

作者： Yilan Cui Xing Xie Yi Liu School of Environment Tsinghua UniversityBeijing 100084China Microsoft Research Asia Microsoft CorporationBeijing 100080China

In this paper,we present a three-step methodological framework,including location identification,bias modification,and out-of-sample validation,so as to promote human mobility analysis with social media *** specifically,we propose ways of identifying personal activity-specific places and commuting patterns in Beijing,China,based on Weibo(China’s Twitter)check-in records,as well as modifying sample bias of check-in data with population synthesis *** independent citywide travel logistic survey is used as the benchmark for validating the *** differences are discerned from Weibo users’and survey respondents’activity-mobility patterns,while there is a large variation of population representativeness between data from the two *** bias modification,the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%.Synthetic data proves to be a satisfactory costeffective alternative source of mobility *** proposed framework can inform many applications related to human mobility,ranging from transportation,through urban planning to transport emission modeling.

关键词： social media Human mobility Population bias Sample reconstruction Data integration

social media RESEARCH:A REVIEW

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Journal of Systems Science and Systems Engineering 2013年第3期22卷 257-282页

作者： Junjie WU Haoyan SUN Yong TAN School of Economics and Management Beihang University Michael G.Foster School of Business University of Washington

social media is fundamentally changing the way people communicate, consume and collaborate. It provides companies a new platform to interact with their customers. In academia, there is a surge in research efforts on understanding its effects. This paper aims to provide a review of current status of social media research. We discuss the specific domains in which the impacts of social media have been examined. A brief review of applicable research methodologies and approaches is also provided.

关键词： social media empirical models experimental methods analytical approaches predictive analytics

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social media and Stock Market Prediction: A Big Data Approach

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Computers, Materials & Continua 2021年第5期67卷 2569-2583页

作者： Mazhar Javed Awan Mohd Shafry Mohd Rahim Haitham Nobanee Ashna Munawar Awais Yasin Azlan Mohd Zain Azlanmz School of Computing Faculty of EngineeringUniversity Teknologi MalaysiaJohorMalaysia Department of Software Engineering University of Management and TechnologyLahorePakistan Collage of Business Abu Dhabi UniversityAbu DhabiUnited Arab Emirates Oxford Center for Islamic Studies The University of OxfordOxfordUK The University of Liverpool Management School The University of LiverpoolLiverpoolUK Department of Computer Engineering National University of TechnologyIslamabadPakistan School of Computing UTM Big Data CentreUniversiti Teknologi MalaysiaJohorMalaysia

Big data is the collection of large datasets from traditional and digital sources to identify trends and *** quantity and variety of computer data are growing exponentially for many *** example,retailers are building vast databases of customer sales *** are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and *** of big data include volume and variety in both structured and unstructured *** this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional *** studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision *** implemented naive Bayes and logistic regression classification *** results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

关键词： Big data analytics artificial intelligence machine learning stock market social media business analytics

social media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： social media epidemic machine learning deep learning health informatics pandemic

social media mining and visualization for point-o f-interest recommendation

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The Journal of China Universities of Posts and Telecommunications 2017年第1期24卷 67-76,86页

作者： Ren Xingyi Song Meina E Haihong Song Junde School of Computing Beijing University of Posts and Telecommunications Beijing 100876 China Engineering Research Center of Information Networks Beijing University of Posts and Telecommunications Beijing 100876 China

With the rapid growth of location-based social networks （LBSNs）, point-of-interest （POI） recommendation has become an important research problem. As one of the most representative social media platforms, Twitter provides various real-life information for POI recommendation in real time. Despite that POI recommendation has been actively studied, tweet images have not been well utilized for this research problem. State-of-the-art visual features like convolutional neural network （CNN） features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image＇s semantics. Unfortunately, they have not been employed for POI recommendation from social websites. Hence, how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open In this paper, we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features. A novel topic model called social media Twitter-latent Dirichlet allocation （SM-TwitterLDA） which jointly models five Twitter features, （i.e., text, image, location, timestamp and hashtag） is designed to discover POIs from the sheer amount of tweets. Moreover, each POI is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

关键词： social media Twitter POI recommendation visualization

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social media video analysis methodology for sarin exposure

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Forensic Sciences Research 2022年第2期7卷 279-284页

作者： Sadik Toprak Emine Yilmaz Can Bulent Altinsoy John Hart Zekeriya Dogan Mustafa Ozcetin Department of Forensic Medicine Istanbul UniversityIstanbulTurkey Department of Medical Pharmacology Bulent Ecevit UniversityZonguldakTurkey Department of Pulmonary Medicine Bulent Ecevit UniversityZonguldakTurkey James Martin Center for Nonproliferation Studies MontereyCAUSA Department of Civil Engineering Bulent Ecevit UniversityZonguldakTurkey Department of Pediatrics Istanbul UniversityIstanbulTurkey

As social media becomes increasingly ubiquitous,many events are recorded and released on social media platforms,including chemical weapon *** develop an objective tool in order to evaluate brief and unstructured social media videos for analysing sarin exposure from a civilian medical pathology *** developed and validated this new questionnaire using a standardized procedure that includes content domain specification,item pool generation,content validity evaluation,a pilot study,and assessment of reliability and *** total,51 sarin attacks and 48 matched videos were ***’s a for all 20 items was 0.75,which indicates adequate internal *** test–retest reliability was 0.96,which indicates good internal *** inter-observer intraclass correlation coefficient was *** verifying sampling adequacy with the Kaiser–Meyer–Olkin measure and the factorability of the items with Barlett’s test of sphericity,a factor analysis was *** to the principal axis factoring,a six-factor solution explained 51.86%of the total *** receiver-operating characteristic curve analysis showed that the Video Score Questionnaire has a sensitivity of 0.817,a specificity of 0.478,and an efficiency of ***,the Video Score Questionnaire is reliable and valid for evaluating sarin attacks from brief and unstructured social media videos.

关键词： Forensic sciences social media sarin scale questionnaire chemical weapon nerve agent YouTube