Data hiding technologies aim to hide the existence of secret information within digital covers such as images by causing unnoticeable degradation to their quality. Reducing the image distortion and increasing the embedding capacity are the main points that the data hiding techniques revolved around. This article proposes two high payload embedding methods with high stego image quality using the Hue‐Saturation‐Value (HSV) colour model. The first method is hue‐based embedding (HBE) that employs the H plane for hiding one or two bits in non‐grey pixels. The second method uses the three HSV components, so it is called three‐planes embedding (TPE). In TPE, one bit is hidden in the least significant bit (LSB) of V of the grey pixels, one or two bits in H of the pixels having low saturation or low brightness and one bit in the LSB of S otherwise. The experiments were conducted on 25 images and the results show that HBE hides more data on average than TPE with its quality reaching 60 dB. TPE achieves quality up to 61 dB and capacity reaches 364 Kb. TPE scores the highest capacity among six state‐of‐the‐art techniques in Red‐Green‐Blue, HSV, Hue‐Saturation‐Intensity and YCbCr spaces with the highest average peak signal to noise ratio midst five of them. By embedding 60, 90, and 120 Kb, this TPE attains the best average quality amid all the methods.

Over the last couple of decades, community question‐answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever‐growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross‐platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed.