Automated Assessment System Research Articles

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning.

The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

Systematic review of automatic assessment systems for resistance-training movement performance: A data science perspective.

The technical performance of resistance-training (RT) movement is commonly monitored through visual assessment and feedback by trained practitioners or by individual self-evaluation. However, both approaches are limited due to their subjectivity, inability to monitor multiple joints simultaneously, and dependency on the assessor's or exerciser's experience and skill. Portable data collection devices and machine learning (ML) have been combined to overcome these limitations by providing objective assessments for RT movement performance. This systematic review evaluates systems developed for providing objective, automatic assessment for RT movements used to improve physical performance and/or rehabilitation in otherwise healthy individuals. Databases searched included Scopus, PubMed and Engineering Village. From 363 papers initially identified, 13 met the inclusion and exclusion criteria. Information extracted from the collated papers included the experimental protocols, data processing, ML model development methodology and movement classification performance. Identified movement assessment systems ranged in classification performance (accuracy of 70%-90% for most classifiers). However, several methodological errors in the development of the ML models were identified, and additional aspects such as model interpretability or generalisability were often neglected. Future ML models should adopt the correct developmental methodology and provide interpretable and generalisable models for application in the RT environment.

A Deep Learning System for Automatic Assessment of Anterior Chamber Angle in Ultrasound Biomicroscopy Images.

PurposeTo develop and assess a deep learning system that automatically detects angle closure and quantitatively measures angle parameters from ultrasound biomicroscopy (UBM) images using a deep learning algorithm.MethodsA total of 3788 UBM images (2146 open angle and 1642 angle closure) from 1483 patients were collected. We developed a convolutional neural network (CNN) based on the InceptionV3 network for automatic classification of angle closure and open angle. For nonclosed images, we developed a CNN based on the EfficienttNetB3 network for the automatic localization of the scleral spur and the angle recess; then, the Unet network was used to segment the anterior chamber angle (ACA) tissue automatically. Based on the results of the latter two processes, we developed an algorithm to automatically measure the trabecular-iris angle (TIA500 and TIA750), angle-opening distance (AOD500 and AOD750), and angle recess area (ARA500 and ARA750) for quantitative evaluation of angle width.ResultsUsing manual labeling as the reference standard, the ACA classification network's accuracy reached 98.18%, and the sensitivity and specificity for angle closure reached 98.74% and 97.44%, respectively. The deep learning system realized the automatic measurement of the angle parameters, and the mean of differences was generally small between automatic measurement and manual measurement. The coefficients of variation of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 measured by the deep learning system were 5.77%, 4.67%, 10.76%, 7.71%, 16.77%, and 12.70%, respectively. The within-subject standard deviations of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 were 5.77 degrees, 4.56 degrees, 155.92 µm, 147.51 µm, 0.10 mm2, and 0.12 mm2, respectively. The intraclass correlation coefficients of all the angle parameters were greater than 0.935.ConclusionsThe deep learning system can effectively and accurately evaluate the ACA automatically based on fully automated analysis of a UBM image.Translational RelevanceThe present work suggests that the deep learning system described here could automatically detect angle closure and quantitatively measure angle parameters from UBM images and enhancing the intelligent diagnosis and management of primary angle-closure glaucoma.

Electrocardiogram Quality Assessment with a Generalized Deep Learning Model Assisted by Conditional Generative Adversarial Networks.

The electrocardiogram (ECG) is widely used for cardiovascular disease diagnosis and daily health monitoring. Before ECG analysis, ECG quality screening is an essential but time-consuming and experience-dependent work for technicians. An automatic ECG quality assessment method can reduce unnecessary time loss to help cardiologists perform diagnosis. This study aims to develop an automatic quality assessment system to search qualified ECGs for interpretation. The proposed system consists of data augmentation and quality assessment parts. For data augmentation, we train a conditional generative adversarial networks model to get an ECG segment generator, and thus to increase the number of training data. Then, we pre-train a deep quality assessment model based on a training dataset composed of real and generated ECG. Finally, we fine-tune the proposed model using real ECG and validate it on two different datasets composed of real ECG. The proposed system has a generalized performance on the two validation datasets. The model’s accuracy is 97.1% and 96.4%, respectively for the two datasets. The proposed method outperforms a shallow neural network model, and also a deep neural network models without being pre-trained by generated ECG. The proposed system demonstrates improved performance in the ECG quality assessment, and it has the potential to be an initial ECG quality screening tool in clinical practice.

Artificial intelligence in education: Addressing ethical challenges in K-12 settings.

Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students’ learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners’ behaviors. Despite the potential benefits of AI to support students’ learning experiences and teachers’ practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students’ learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers—i.e., the Massachusetts Institute of Technology’s (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students’ understanding of AI and ethics.

Modular deep neural networks for automatic quality control of retinal optical coherence tomography scans

Retinal optical coherence tomography (OCT) with intraretinal layer segmentation is increasingly used not only in ophthalmology but also for neurological diseases such as multiple sclerosis (MS). Signal quality influences segmentation results, and high-quality OCT images are needed for accurate segmentation and quantification of subtle intraretinal layer changes. Among others, OCT image quality depends on the ability to focus, patient compliance and operator skills. Current criteria for OCT quality define acceptable image quality, but depend on manual rating by experienced graders and are time consuming and subjective. In this paper, we propose and validate a standardized, grader-independent, real-time feedback system for automatic quality assessment of retinal OCT images. We defined image quality criteria for scan centering, signal quality and image completeness based on published quality criteria and typical artifacts identified by experienced graders when inspecting OCT images. We then trained modular neural networks on OCT data with manual quality grading to analyze image quality features. Quality analysis by a combination of these trained networks generates a comprehensive quality report containing quantitative results. We validated the approach against quality assessment according to the OSCAR-IB criteria by an experienced grader. Here, 100 OCT files with volume, circular and radial scans, centered on optic nerve head and macula, were analyzed and classified. A specificity of 0.96, a sensitivity of 0.97 and an accuracy of 0.97 as well as a Matthews correlation coefficient of 0.93 indicate a high rate of correct classification. Our method shows promising results in comparison to manual OCT grading and may be useful for real-time image quality analysis or analysis of large data sets, supporting standardized application of image quality criteria.

Using a distributed systems laboratory to facilitate students' cognitive, metacognitive and critical thinking strategy use

AbstractBackgroundRecent research in online settings reports that supporting self‐regulated learning (SRL) strategy use could lead to greater online academic success. A growing number of studies have started to investigate SRL supports in online environments recently, which indicates a great interest in this matter. Though several systems for automatic assessment of programming have been developed, there is hardly any study that has investigated how an automated assessment tool for distributed programming could facilitate students' SRL strategies.ObjectivesThis study examined the ways our online Distributed Systems Laboratory (DSLab) tried to enhance students' SRL strategies in an authentic long‐term online educational experience.MethodsWe applied an experimental research design, involving 111 university students who performed a programming assignment using DSLab. A customized questionnaire was used to collect data from all students.Results and ConclusionsThe statistical analyses revealed that DSLab tool managed to facilitate students' cognitive and meta‐cognitive strategy use to a certain extent and critical thinking strategy use to a fairly large extent.ImplicationsThough more experimental results are needed to delve more deeply into these findings, this study provides relevant implications for online distributed (or general) programming course teachers who seek to increase students' SRL strategies in this field.

Development of a Hand Motion-based Assessment System for Endotracheal Intubation Training.

Endotracheal intubation (ETI) is a procedure to manage and secure an unconscious patient's airway. It is one of the most critical skills in emergency or intensive care. Regular training and practice are required for medical providers to maintain proficiency. Currently, ETI training is assessed by human supervisors who may make inconsistent assessments. This study aims at developing an automated assessment system that analyzes ETI skills and classifies a trainee into an experienced or a novice immediately after training. To make the system more available and affordable, we investigate the feasibility of utilizing only hand motion features as determining factors of ETI proficiency. To this end, we extract 18 features from hand motion in time and frequency domains, and also 12 force features for comparison. Subsequently, feature selection algorithms are applied to identify an ideal feature set for developing classification models. Experimental results show that an artificial neural network (ANN) classifier with five hand motion features selected by a correlation-based algorithm achieves the highest accuracy of 91.17% while an ANN with five force features has only 80.06%. This study corroborates that a simple assessment system based on a small number of hand motion features can be effective in assisting ETI training.

An Automatic Assessment System of Diabetic Foot Ulcers using Sensors

An Automatic Assessment System of Diabetic Foot Ulcers using Sensors

Automatic quantification package (Hone Graph) for phantom-based image quality assessment in bone SPECT: computerized automatic classification of detectability.

We previously developed a custom-design thoracic bone scintigraphy-specific phantom ("SIM2 bone phantom") to assess image quality in bone single-photon emission computed tomography (SPECT). We aimed to develop an automatic assessment system for imaging technology in bone SPECT and demonstrate the validity of this system. Four spherical lesions of 13-, 17-, 22-, and 28-mm diameters in the vertebrae of SIM2 bone phantom simulating the thorax were filled with radioactivity (target-to-background ratio: 4). Dynamic SPECT acquisitions were performed for 15min; reconstructions were performed using ordered subset expectation maximization at 3-15-min timepoints. Consequently, 216 lesions (54 SPECT images) were obtained: 120 and 96 lesions were used for software development and validation, respectively. The developed software used statistical parametric mapping to rigidly register and automatically calculate quantitative indexes (contrast-to-noise ratio, % coefficient of variance, % detectability equivalence volume, recovery coefficient, target-to-normal bone ratio, and full width at half maximum). A detectability score (DS) was used to define the four observation types (4, excellent; 3, adequate; 2, average; 1, poor) to score hot spherical lesions. The gold standard for DSs was independently classified by three experienced board-certified nuclear medicine technologists using the four observation types; thereafter, a consensus regarding the gold standard for DSs was reached. Using 120 lesions for development, decision tree analysis was performed to determine DS based on the quantitative indexes. We verified the validation of the quantitative indexes and their threshold values for automatic classification using 96 lesions for validation. The trends in the automatically calculated quantitative indices were consistent. Decision tree analysis produced four terminal groups; two quantitative indexes (% detectability equivalence volume and contrast-to-noise ratio) were used to classify DS. The automatically classified DSs exhibited an almost perfect agreement with the gold standard. The percentage agreement and kappa coefficient were 91.7% and 0.93, respectively, in 96 lesions for validation. The developed software automatically classified the detectability of hot lesions in the SIM2 bone phantom using the automatically calculated quantitative indexes, suggesting that this software could provide a means to automatically perform detectability analysis after data input that is excellent in reproducibility and accuracy.

Automated two-stage continuous decision support model using exploratory factor analysis-MACBETH-SMART: an application of contractor selection in public sector construction

Public sector client marks contractor selection decisions on technical and financial bid considerations where efficient use of public resources is never unheeded. A plethora of past studies has developed two-stage models; however, continuous assessment of contractors is disregarded, and the models compromise on the discontinuous progression that partially recognizes the prominence of the technical stage in the selection process. This research aims to develop a novel automated two-stage continuous decision model for contractors’ assessment and selection where each contractor would be assessed on corresponding performance assessment grading levels. Exploratory Factor Analysis (EFA) assimilated with MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) employed to assess the model criteria, whereas, criteria assessment stage is developed using a novel hybrid combination of SMART (Simple Multi-Attribute Rating Technique), which in turn entails the EFA-MACBETH-SMART triplet-combination. The model encompasses extensive model criteria; thus, 76 model criteria were investigated and evaluated. Final selection of a contractor is proposed on technical bid/financial bid ratio mechanisms based on performance levels such as RT/F: 80/20; 75/25; 70/30; 65/35; and 60/40. A hypothetical case is encompassed to portray the operational mechanism of the automated assessment system. Findings from the model unveil that continuous progression of technical assessment stage in final selection make justice with the highly qualified contractors, and the likelihood of project success increases. The developed model further conclude that technically highest bidders may be awarded the contract if additionally offers a feasible bid. The developed model preserves the concept of efficient use of public resources alongside supporting the technically highest bidders.

Towards development of a system for automatic assessment of the quality of a question paper

In this paper, we present a system for automatic evaluation of the quality of a question paper. Question paper plays a major role in educational assessment. The quality of a question paper is crucial to fulfilling the purpose of the assessment. In many education sectors, question papers are prepared manually. A prior analysis of a question paper might help in finding the errors in the question paper, and better achieving the goals of the assessment. In this experiment, we focus on higher education in the technical domain. First, we conducted a student survey to identify the key factors that affect the quality of a question paper. The top factors we identified are question relevance, question difficulty, and time requirement. We explored the strategies to handle these factors and implemented them. We employ various concepts and techniques for the implementation. The system finally assigns a numerical quality score against these factors. The system is evaluated using a set of question papers collected from various sources. The experimental results show that the proposed system is quite promising.

Automatic speaker independent dysarthric speech intelligibility assessment system

Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery. The loss of fine control over muscles that assist the movement of lips, vocal chords, tongue and diaphragm results in abnormal speech delivery. One can assess the severity level of dysarthria by analyzing the intelligibility of speech spoken by an individual. Continuous intelligibility assessment helps speech language pathologists not only study the impact of medication but also allows them to plan personalized therapy. It helps the clinicians immensely if the intelligibility assessment system is reliable, automatic, simple for (a) patients to undergo and (b) clinicians to interpret. Lack of availability of dysarthric data has resulted in development of speaker dependentautomatic intelligibility assessment systems which requires patients to speak a large number of utterances. In this paper, we propose (a) a cost minimization procedure to select an optimal (small) number of utterances that need to be spoken by the dysarthric patient, (b) four different speaker independent intelligibility assessment systems which require the patient to speak a small number of words, and (c) the assessment score is close to the perceptual score that the Speech Language Pathologist (SLP) can relate to. The need for small number of utterances to be spoken by the patient and the score being relatable to the SLP benefits both the dysarthric patient and the clinician from usability perspective.

Weighted Mel frequency cepstral coefficient based feature extraction for automatic assessment of stuttered speech using Bi-directional LSTM

Objective: To propose a system for automatic assessment of stuttered speech to help the Speech Language Pathologists during their treatment of a person who stutters. Methods: A novel technique is proposed for automatic assessment of stuttered speech, composed of feature extraction based on Weighted Mel Frequency Cepstral Coefficient and classification using Bi-directional Long-Short Term Memory neural network. It mainly focuses on detecting prolongation and syllable, word, and phrase repetition in stuttered events. Findings: This study has discussed and performed a comparative analysis of WMFCC feature extraction method with different extensions of widely used MFCC, namely, Delta, and Delta-Delta cepstrum. The comparison of speech parameterization techniques is carried out based on the effect of different frame lengths, percentage of window overlapping, and preemphasis filter alpha value. The experimental investigation elucidated that WMFCC outperforms the other feature extraction methods and provides an average recognition accuracy of 96.67%. 14-dimensional WMFCC achieves a low computational overhead compared to conventional 42-dimensional MFCC, including Delta and Delta-delta cepstrum. Application: The integration of Weighted MFCC based speech feature extraction and deep learning Bi-LSTM based classification techniques proposed in this study are more efficient for introducing an optimal model to automatically classify the stuttered events such as prolongation and repetition. Keywords: Stuttering; MFCC; Delta MFCC; WMFCC; BiLSTM

Automatic and Objective Assessment of Motor Skills Performance in Flexible Bronchoscopy

Background: Motor skills have been identified as a useful measure to evaluate competency in bronchoscopy. However, no automatic assessment system of motor skills with a clear pass/fail criterion in flexible bronchoscopy exists. Objectives: The objective of the study was to develop an objective and automatic measure of motor skills in bronchoscopy and set a pass/fail criterion. Methods: Participants conducted 3 bronchoscopies each in a simulated setting. They were equipped with a Myo Armband that measured lower arm movements through an inertial measurement unit, and hand and finger motions through electromyography sensors. These measures were composed into an objective and automatic composite score of motor skills, the motor bronchoscopy skills score (MoBSS). Results: Twelve novices, eleven intermediates, and ten expert bronchoscopy operators participated, resulting in 99 procedures available for assessment. MoBSS was correlated with a higher diagnostic completeness (Pearson’s correlation, r = 0.43, p < 0.001) and a lower procedure time (Pearson’s correlation, r = −0.90, p < 0.001). MoBSS was able to differentiate operator performance based on the experience level (one-way ANOVA, p < 0.001). Using the contrasting groups’ method, a passing score of −0.08 MoBSS was defined that failed 30/36 (83%) novice, 5/33 (15%) intermediate, and 1/30 (3%) expert procedures. Conclusions: MoBSS can be used as an automatic and unbiased assessment tool for motor skills performance in flexible bronchoscopy. MoBSS has the potential to generate automatic feedback to help guide trainees toward expert performance.

Answer Book Valuation Using Semantic Similarity

Conventional examination systems are a tedious task for the universities and the faculties. It takes a lot of time and human effort for the valuation of the answer books, updation of the marks and the declaration of the results. Automation of descriptive answer book assessment would be useful for universities and academic institutions to simplify the valuation system to a large extend. It ensures a uniform valuation and also helps to publish the results without much delay after the examination. We design a system for automatic assessment of descriptive answer books of technical subjects. Semantic similarity is a metric used to assess the similarity between documents and it gives the degree of similarity as a numeric value. It can be used to value the students’ answer books by checking the similarity with original answers given in the answer key and then award appropriate marks.

Automativ assessment of systolic cardiac performance using PEP/LVET index

Congestive cardiac failure is one of the deadliest diseases in the world, with more than 26 million patients. Echocardiogram and angiography consider as the gold standards for heart failure diagnosis. Nevertheless, they are not commonly used for long-term follow up as they need highly skilled and experienced operator. In this paper, a simple and low-cost system for automatic assessment of systolic cardiac performance using systolic cardiac intervals is presented. The proposed system utilized electrocardiogram (ECG) and phonocardiogram (PCG) to calculate pre-ejection period (PEP) and left ventricle ejection time (LVET). The ratio between PEP and LVET was computed to assess the performance of the systolic cardiac function. ECG and PCG were acquired using a commercial stethoscope which was modified to convert PCG auscultation to electrical signals. ECG and PCG signals were digitized and transferred to a personal computer. A custom MATLAB application was designed to display the acquired ECG and PCG, and to compute PEP, LVET, and PEP/LVET ratio. The system was tested on 17 healthy subjects and results showed high agreement between the systolic heart function status assessed by the proposed system and the corresponding echocardiography results. These results imply that the proposed system could be used for long-term follow up for patients with congestive heart failure.

Automatic Assessment of Keel Bone Damage in Laying Hens at the Slaughter Line.

Simple SummaryKeel bone damage (KBD) is very prevalent in commercial laying hen flocks with a wide range of affected hens/flock. It can cause pain, and affected hens have been found to be less mobile. The assessment of this animal welfare indicator provides important feedback for the farmer about flock health and consequently on the need for interventions. However, the assessment of keel bone damage is time-consuming, and prior training is needed in order to gain reliable results. Optical detection methods can be a means to automatedly score hens at the slaughter line with high sample sizes and in a standardized way. We developed and validated an automatic 3D camera-based detection system. While it generally underestimates the presence of KBD due to the purely visual assessment and technical constraints, it nevertheless shows good accuracy and high correlation of prevalences with those visually determined by a trained human assessor. Therefore, this system opens up opportunities to better monitor and combat a severe animal welfare problem in the long-term.Keel bone damage (KBD) can be found in all commercial laying hen flocks with a wide range of 23% to 69% of hens/flock found to be affected in this study. As KBD may be linked with chronic pain and a decrease in mobility, it is a serious welfare problem. An automatic assessment system at the slaughter line could support the detection of KBD and would have the advantage of being standardized and fast scoring including high sample sizes. A 2MP stereo camera combined with an IDS imaging color camera was used for the automatic assessment. A trained human assessor visually scored KBD in defeathered hens during the slaughter process and compared results with further human assessors and automatic recording. In a first step, an algorithm was developed on the basis of assessments of keel status of 2287 hens of different genetics with varying degrees of KBD. In two optimization steps, performance data were calculated, and flock prevalences were determined, which were compared between the assessor and the automatic system. The proposed technique finally reached a sensitivity of 0.95, specificity of 0.77, accuracy of 0.86 and precision of 0.81. In the last optimization step, the automatic system scored on average about 10.5% points lower KBD prevalences than the human assessor. However, a proposed change of scoring system (setting the limit for KBD at 0.5 cm deviation from the straight line) would lower this deviation. We conclude that the developed automatic scoring technique is a reliable and potentially valuable tool for the assessment of KBD.

Utilizing social virtual reality robot (V2R) for music education to children with high-functioning autism

Virtual Reality (VR) technology is a growing technology that has been used in various fields of psychology, education, and therapy. One group of potential users of VR are children with autism who need education and have poor social interactions; this technology could help them improve their social skills through real-world simulation. In this study, we evaluated the feasibility of conducting virtual music education programs with automatic assessment system for children with autism at treatment/research centers without the need to purchase a robot, resulting in the possibility of offering schedules on a larger scale and at a lower cost. Intervention sessions were conducted for five children with high-functioning autism ranging in age from 6 to 8 years old during 20 weeks which includes a baseline session, a pre-test, training sessions, a post-test, and a follow-up test. Each music education sessions involved teaching different notes and pieces of music according to the child’s cooperation, accuracy, and skill level utilizing virtual reality robots and virtual musical instruments. Actually, by analysis of psychological tests, and questionnaires conducted by a psychologist, we observe slight improvements in cognitive skills because of the ceiling effect. Nevertheless, the effectiveness of the proposed method was proved by conducting statistical analysis on the child’s performance data during the music education sessions which were obtained by using both video coding and the proposed automatic assessment system. Consequently, a general upward trend in the musical ability of participants was shown to occur in these sessions, which warrants future studies in this field.

Social Media and Microblogs Credibility: Identification, Theory Driven Framework, and Recommendation

Social media microblogs are extensively used to get news and other information. It brings the real challenge to distinguish that what particular information is credible. Especially when user authenticity is hidden, due to the microblog’s anonymity feature. Low credibility content creates an imbalance in society. Therefore many research studies are conducted to assess automatic microblog’s credibility but the majority of them offer different concepts of credibility and the problem seems unresolved. Credibility is multi-disciplinary, hence there is no generalized or accepted credibility concept with all its necessary and detailed constructs/components. Therefore, it is necessary to understand the complete anatomy of information credibility from different disciplines. It is accomplished here through an in-depth and organized study of all the problem dimensions for the identification of comprehensive and necessary credibility constructs. The framework is also proposed based on the identified constructs. It adheres to these constructs and presents their inter-relationships. It is believed that the framework would provide the necessary building blocks for implementing an effective automatic credibility assessment system. The framework is generic to social media and specifically implemented for microblogs. It is completely transformed up to features level, in the context of microblogs. Regarding automatic credibility assessment, it is proposed after detailed analysis that the attempt should be made for hybrid models combining feature-based and graph-based approaches. It is observed that quite a few surveys in the literature focus on some limited aspects of microblogs credibility but no literature survey and fundamental study exists that consolidates the work done. To understand the broader domain of credibility and consolidate the work in this area that can lead us to a suitable framework, we explored the existing literature from different disciplines for the said objectives. We categorized them along various dimensions, developed taxonomy, identified gaps and challenges, proposed a solution, developed a theory-driven framework with its transformation to microblogs, and suggested key areas of research.