Internet Of Multimedia Things Research Articles

Internet of multimedia things (IoMT): A review

Internet of multimedia things (IoMT): A review

A Novel Approach for Sentiment Analysis of a Low Resource Language Using Deep Learning Models

Sentiment analysis is a process of dealing with people's opinions, remarks, and comments to extract valuable insights from them. Sentiment analysis can be used for various purposes like market analysis, campaign monitoring, decision-making, etc. In recent years, there has been much research on sentiment classification, particularly in English. However, these existing approaches used for the English language cannot be applied to the Urdu language. The substantial rise in communication traffic, including audio, text, video, and pictures, has significantly shifted the Internet of Things (IoT) from scalar to Multimedia Internet of Things (MIoT). So far, the integration of MIoT and NLP systems has received less attention, but it has evolved as a novel research paradigm for smart applications. This article proposes deep learning techniques for sentence-level Urdu sentiment analysis (Urdu SA) for MIoT. Our approach consists of various phases, i.e., data gathering, text preprocessing, model training, testing, and evaluation. A data set of 25 thousand Urdu reviews are used for training the proposed models. This data set is built by scraping various Urdu blogs and social media platforms, and some part of the IMDB data set is used after translating it into the Urdu language. Native Urdu speakers do data annotation, and various preprocessing techniques, i.e., tokenization, stemming, etc., are applied. The two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM), are trained on preprocessed Urdu reviews to find their sentiments in this article. Both models are tested using various combinations of hyperparameters, and each model's accuracy and F1 scores are evaluated. The study results show that the LSTM model outperforms the CNN model by achieving a 96% accuracy and 91% F1 score.

IoT-enabled image captioning with deep learning for healthcare domain

<p>The ever-increasing volume of medical images greatly strains clinicians who are in the process of reviewing it and writing reports. It would be more efficient and cost-effective if an image captioning model could automatically create report drafts from matching photos, thereby relieving physicians from this tedious work. The Internet of things (IoT) has switched its emphasis from its initial binary concept to that of the Internet of multimedia things (IoMT) because of the explosive rise of multilingual-on-demand data in various sound, footage, picture forms. This work proposed a deep learning-based image caption network (DL-ICN) for healthcare domain. The work originality is shown using DL to identify various class labels of the patient X-ray and ECG images. With the help of bilateral encoder representations from transformers (BERT) method for captioning pictures, a detailed written summary of a person’s medical picture may be generated automatically. Results of simulations showed that the proposed model achieved good compression performance, good quality reconstruction and good classification results for image captioning.</p>

A Bitcoin-based Secure Outsourcing Scheme for Optimization Problem in Multimedia Internet of Things

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency.

Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems

In the context of the increasing demand for Internet of Multimedia Things (IoMT) services, rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) technologies have been considered potential networking enablers to provide ultra-throughput wireless access. However, challenges arise due to the heterogeneity of IoMT devices and arbitrary network quality changes, resulting in unwanted service quality fluctuations and downgradation. This study addresses this problem by jointly optimizing wireless resource allocation and bitrate adaptation with Deep Reinforcement Learning (DRL)-based QoE management for IRS-aided RSMA-enabled IoMT streaming systems. We formulated the problem as a Markov decision process (MDP) and apply Proximal Policy Optimization (PPO) method to flexibly adjust IoMT bitrate, transmission beamforming, IRS phase shift, and RSMA parameters. As a result, our algorithm mitigates overestimation of client-side bandwidth, leading to smoother playback and reduced quality fluctuations. Simulations show that our approach outperforms baseline methods in terms of video resolution (up to 2.5 times) and achievable sum-rate (up to 50%), contributing to a superior streaming experience in IoMT systems.

Multi-Criteria Decision-Making Framework with Fuzzy Queries for Multimedia Data Fusion

Multimedia Internet of Things (MIoT) is widely explored in many smart applications for connectivity with wireless communication. Such networks are not like ordinary networks because it has to collect a massive amount of data and are further forwarded to processing systems. As MIoT is very limited in terms of resources for healthcare, smart homes, etc., therefore, energy efficiency with reliable data transmission is a significant research challenge. As smart applications rely on bounded constraints, therefore duplicate and unnecessary data transmission should be minimized. In addition, the timely delivery of data in crucial circumstances has a significant impact on any proposed system. Consequently, this paper presents a fuzzy logic-based edge computing framework to provide cooperative decision-making while avoiding inefficient use of the sensing power of smart devices. The proposed framework can be applied to critical applications to improve response time and processing cost. It consists of the following two functional components: Firstly, it provides the automated routing process with a natural language interface at the sink node. Secondly, to ensure reasonable performance, it also transmits semantic data between sensors using fuzzy queries and security. According to the performance evaluation, the proposed framework significantly outperformed related studies in terms of energy consumption, packet overhead, network throughput, and end-to-end delay.

Multimodal High-Order Relationship Inference Network for Fashion Compatibility Modeling in Internet of Multimedia Things

Recent progress in artificial intelligence (AI) have broadened various intelligent application scenarios on the Internet of Multimedia Things (IoMT). Due to the urgent demands for intelligence in the online fashion industry, using AI techniques to explore user’s clothing collocations from fashion data generated by the IoMT system is a challenging task. Under the background, fashion compatibility modeling (FCM), which aims to estimate the matching degree of a given outfit, has attracted great attention in the multimedia analysis field. However, most of the studies often fail to fully leverage multimodal content or ignore the sparse associations between fashion items. In this paper, we propose a novel multimodal high-order relationship inference network (MHRIN) for fashion compatibility modeling task. In MHRIN, we focus on enriching multimodal representations of fashion items by means of incorporating the category correlations and injecting high-order item-item connectivity. Concretely, considering that fashion collocations depend on the semantic relevance patterns between categories, we design a category correlations learning module to adaptively learn category representations. On this basis, multiple modality representations are aggregated by a hierarchical multimodal fusion module to generate visual-semantic embeddings. To address the item-item matching interactions issue, we further refine the final representations by a high-order message propagation module to absorb rich connection information. Experiments on the publicly available dataset demonstrate the superiority of our MHRIN over state-of-the-art methods.

Secure smart home architecture for ambient-assisted living using a multimedia Internet of Things based system in smart cities.

Recent advances in smartphones and remote monitoring based on the Internet of Things (IoT) have enabled improved multidimensional intelligent services. The advent of IoT-based wearable and multimedia sensors has prevented millions of mishapsthrough seamless and systematic monitoring. An IoT-based monitoring system is composed of several sensor devices to measure vital signs, fall detection, energy consumption, and visual recognition. As the data collected by the sensors are transmitted to cloud storage through the Internet, data security is a major concern when transmitting data from remote locations. To improve data security and prediction accuracy, in this study, we proposed a smart and secure multimedia IoT monitoring system for smart homes backed up by smart grid supervisory control and data acquisition (SCADA). The proposed system employs state-of-the-art IoT microcontrollers and hardware devices and integrates them in a manner that significantly affects the accuracy and speed of the entire system. Furthermore, the information gathered from IoT is securely transferred through private channels and stored on the cloud, which can be accessed authentically and reliably using an information system built into an IoT application. The output was extensively compared in terms of power consumption and delivery ratio, which were based on the values collected with sequence numbers. The comparative analysis demonstrated that the proposed approach provides increased prediction accuracy and better security. Hence, the proposed powerefficient prototype model monitors the entire smart home environment in real time and serves as an early warning system for critical situations.

Multimedia Data Transmission with Secure Routing in M-IOT-based Data Transmission using Deep Learning Architecture

The proliferation of Multimedia Internet of Things (MIoT) introduces unprecedented challenges in ensuring the security and reliability of data transmission. This paper proposes a novel approach, the Hierarchical Trust Aware Blockchain (HTaB) model, designed to address the complex requirements of MIoT networks. The HTaB model combines the strengths of blockchain technology and a hierarchical trust structure to enhance data integrity, secure routing, and trust management. The integrated blockchain component of the HTaB model is rigorously evaluated, demonstrating the validation of eight blocks per second, a consensus mechanism time of 15 milliseconds, and an average block creation time of 12 seconds. These metrics affirm the efficiency and responsiveness of the blockchain in safeguarding MIoT data transmissions. Simulation results across varying network sizes showcase the scalability of the HTaB model, indicating high throughput, low Latency, and a robust packet delivery ratio. Comparative analyses against Conventional RPL and a Generic Trust Scheme underscore the competitive advantages of the HTaB model, exhibiting superior energy efficiency, higher throughput, a remarkable packet delivery ratio, and minimal packet loss. This paper comprehensively explains the HTaB model’s capabilities in securing and efficiently transmitting multimedia data within MIoT networks. The proposed model presents a promising solution for addressing the evolving challenges in MIoT environments, providing a foundation for further research and practical implementation in real-world scenarios.

WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things

Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent.

A light-weight skeleton human action recognition model with knowledge distillation for edge intelligent surveillance applications

Skeleton based human action recognition has evolved as one of the most important applications in multimedia IoT system. However, it requires extensive computation resource including high performance computing unites and large memory to train a deep mode with large number of parameters, which seriously limits it effectiveness and efficiency for edge intelligence multimedia IoT applications. In this paper, a knowledge distillation based light-weight deep model is proposed for skeleton human action recognition to meet the edge multimedia IoT applications. It can get competitive recognition performance in terms of learning accuracy for combination of AI model and edge surveillance equipment. On the one hand, to achieve desirable accuracy, we propose a deep pose-transition image representation method based on two-stream spatial–temporal architecture, which can mine the hidden features of color texture images in spatial and temporal domain, and fuse them for comprehensive discrimination before final classification. On the other hand, to increase the transfer learning ability to the student model on the edge device, we use tucker decomposition to weak the teacher model during knowledge transfer learning process. Finally, in order to validate the effectiveness of our proposal, we conducted extensive experiments to evaluate the proposed approach. The experimental results demonstrate that our proposal can realize deep model miniaturization to meet the requirement of edge multimedia IoT system and achieve the competitive performance.

Analysis of the Security of Internet of Multimedia Things in Wireless Environment

The Internet of Things (IoT) and real-time flexibility improve people’s lives, and IoT applications rely heavily on multimedia sensors and devices. An interconnected network of IoT multimedia devices has made the Internet of Medical Things (IoMT). It creates massive data distinct from what the Internet of Things (IoT) produced. Smart traffic monitoring and smart hospitals are only a few examples of real-time deployment applications. IoMT data and decision-making must be made quickly since it directly impacts human life. The security heterogeneity of optimization issues is a significant challenge for enabling multimedia applications on the IoT. The IoMT has difficulty achieving low-cost data collecting while maintaining data security. An Internet of Multimedia Things in a wireless environment (IoMT-WE) system decreases the bandwidth and privacy risk caused by the revocation list, ensures the integrity of batch verification information, and corresponds with Vehicular ad hoc network (VANET) security performance. The proposed method uses random subsampling and chaotic convolution to collect numerous images. The sampling method is safe since the measurement matrix is controlled by chaos. As part of the IoMT architecture, wireless multimedia sensor nodes can be more easily deployed over the long term for real-time multimedia. The Wireless Multimedia Sensor Network (WMSN) comprises nodes that can capture both multimedia and non-multimedia data. The ioMT-WE system has been tested and found to be secure and effective.

CPS: Cross-interface network Partitioning and Scheduling towards QoS-aware data flow delivery in multimedia IoT

In the Internet of Things (IoT), multimedia traffic for audio, image, and video accounts for the largest proportion (over 78.7%) of the total traffic, bringing forward the vision of multimedia IoT (M-IoT). As part of the realization of loT, M-IoT is a general network paradigm that constitutes many smart objects equipped with the capability to collect multimedia data from the physical environment and deliver the data to other things. To satisfy a certain level of user experience, Quality of Service (QoS) is required to be regulated to ensure acceptable delivery of the multimedia content. As the most widely-used wireless technology, WiFi has been recommended for IoT communications for its high data rate, native IP compatibility, and good reusability of the existing infrastructures. However, WiFi suffers from channel contention, especially during multi-hop communications, which degrades the QoS performance and hinders its use for many M-IoT services. Although numerous protocols have been proposed to mitigate WiFi contention, they often consume much WiFi bandwidth for network control, lowering the level of achievable QoS performance. To address this issue, we propose a distributed Cross-interface network Partitioning and Scheduling (CPS) protocol, which leverages the co-existing ZigBee communications to divide the network into partitions and allows only one node in each partition to use its WiFi interface to transmit data at any time, for bandwidth-efficient and delay-constrained data flow delivery in M-IoT. A prototype node is implemented by integrating COTS ZigBee and WiFi interfaces into a BeagleBone Green wireless platform for IoT. Extensive field experiments are conducted in a multi-hop network of 24 prototype nodes that deliver real multimedia data (images and videos). The experiment results show that CPS outperforms the standard WiFi and a state-of-the-art contention control scheme (by 62.6% and 26.4% under high data traffic, respectively) in terms of a QoS metric capturing two basic performance metrics (i.e., bandwidth efficiency and end-to-end delay) of multi-hop communications, while retaining fair QoS performance and high energy efficiency.

A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm

The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT.

Guest Editors’ Introduction for Special Issue on Applications of Computational Linguistics in Multimedia IoT Services

introduction Guest Editors’ Introduction for Special Issue on Applications of Computational Linguistics in Multimedia IoT Services Authors: Quan Z. Sheng School of Computing, Macquarie University, Sydney, Australia School of Computing, Macquarie University, Sydney, Australia 0000-0002-3326-4147Search about this author , Arun Kumar Sangaiah National Yunlin University of Science and Technology, Taiwan National Yunlin University of Science and Technology, Taiwan 0000-0002-0229-2460Search about this author , Ankit Chaudhary Department of Computer Science, University of Missouri at Saint Louis, USA Department of Computer Science, University of Missouri at Saint Louis, USA 0000-0001-7510-1963Search about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 23Issue 2Article No.: 24pp 1–3https://doi.org/10.1145/3591355Published:23 May 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Stance-level Sarcasm Detection with BERT and Stance-centered Graph Attention Networks

Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.

VoiceTalk: Multimedia-IoT Applications for Mixing Mandarin, Taiwanese, and English

The voice-based Internet of Multimedia Things (IoMT) is the combination of IoT interfaces and protocols with associated voice-related information, which enables advanced applications based on human-to-device interactions. An example is Automatic Speech Recognition (ASR) for live captioning and voice translation. Three major issues of ASR for IoMT are IoT development cost, speech recognition accuracy, and execution time complexity. For the first issue, most non-voice IoT applications are upgraded with the ASR feature through hard coding, which are error prone. For the second issue, recognition accuracy must be improved for ASR. For the third issue, many multimedia IoT services are real-time applications and, therefore, the ASR delay must be short. This article elaborates on the above issues based on an IoT platform called VoiceTalk. We built the largest Taiwanese spoken corpus to train VoiceTalk ASR (VT-ASR) and show how the VT-ASR mechanism can be transparently integrated with existing IoT applications. We consider two performance measures for VoiceTalk: speech recognition accuracy and VT-ASR delay. For the acoustic tests of PAL-Labs, VT-ASR's accuracy is 96.47%, while Google's accuracy is 94.28%. We are the first to develop an analytic model to investigate the probability that the VT-ASR delay for the first speaker is complete before the second speaker starts talking. From the measurements and analytic modeling, we show that the VT-ASR delay is short enough to result in a very good user experience. Our solution has won several important government and commercial TV contracts in Taiwan. VT-ASR has demonstrated better Taiwanese Mandarin speech recognition accuracy than famous commercial products (including Google and Iflytek) in Formosa Speech Recognition Challenge 2018 (FSR-2018) and was the best among all participating ASR systems for Taiwanese recognition accuracy in FSR-2020.

A Study on Energy Efficiency of a Distributed Processing Scheme for Image-Based Target Recognition for Internet of Multimedia Things

A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. In depth, image-based sensing and transmission in IoMT significantly drain the sensor energy and overwhelm the network with redundant data. Event-based sensing schemes can be used to provide efficient data transmission and an extended network lifetime. This paper proposes a novel approach for distributed event-based sensing achieved by a cluster of processing nodes. The proposed scheme aims to balance the processing load across the nodes in the cluster. This study demonstrates the adequacy of distributed processing to extend the lifetime of the IoMT platform and compares the efficiency of Haar wavelet decomposition and general Fourier descriptors (GFDs) as a feature extraction module in a distributed features-based target recognition system. The results show that the distributed processing of the scheme based on the Haar wavelet transform of the image outperforms the scheme based on a general Fourier shape descriptor in recognition accuracy of the target as well as the energy consumption. In contrast to a GFD-based scheme, the recognition accuracy of a Haar-based scheme was increased by 26%, and the number of sensing cycles was increased from 40 to 70 cycles, which attests to the adequacy of the proposed distributed Haar-based processing scheme for deployment in IoMT devices.

SecureCam: Selective Detection and Encryption Enabled Application for Dynamic Camera Surveillance Videos

Using dynamic surveillance cameras for security has significantly increased the privacy concerns for captured individuals. Malicious users may misuse these videos by performing Replay and/or Man-in-the-Middle attacks during storage or recording over the network. Considering these risks, this paper proposes an effective security application SecureCam based on selective detection (focused moving objects) and protection using encryption. For object detection, this paper implements a novel low computational unsupervised learning algorithm i.e. Motion-Fusion (MF) for more precise motion detection in the mobile camera videos. After that, selective encryption (SE) is applied by the lightweight Chacha20 cipher to the detected video parts. Proposed SecureCam is extensively evaluated based on performance analysis, security analysis and computational complexity. For object detection, the comparative evaluation shows that the MF algorithm outperforms traditional state-of-the-art dense optical flow (DOF) algorithm with an average (mean) difference increase: in the accuracy of 54%; and in the precision of 42% making it computationally effective for such videos. The visual results with 21% encryption space ratio (ESR) indicate that the videos are sufficiently protected against identification. Overall comparative evaluation with existing approaches also affirm the significance and utility of proposed SecureCam for internet of multimedia things (IoMT) environment.

Detecting Dravidian Offensive Posts in MIoT: A Hybrid Deep Learning Framework

Hate speech and Offensive Posts (OP) detection on Smart Multimedia Internet of Things (MIoT) have been an active issue for researchers. MIoT media texts in non-native English-speaking countries are often code-mixed or script mixed/switched. This paper proposes an ensemble-based Deep Learning (DL) framework comprised of a Convolutional Neural Network (CNN) and a Dense Neural Network (DNN) for identifying hate and OP in Malayalam Code-Mixed (MCM), Tamil Code-Mixed (TCM), and Malayalam Script-Mixed (MSM) MIoT media postings. Word-level and character-level features are utilized in the convolutional neural network. In contrast, the dense neural network uses character-level Term Frequency-Inverse Document Frequency (TF-IDF) features. The inclusion of character-level features in the proposed ensemble framework resulted in state-of-the-art performance for TCM and MCM datasets, with weighted F 1 -score of 0.91 and 0.78, respectively, and comparable performance for MSM posts, with a weighted F 1 -score of 0.95.