DETECTION OF HATE SPEECH ON SOCIAL MEDIA UTILIZING MACHINE LEARNING

Hate speech is a form of negative communication intended to harm people and communities. Hate speech is quite common in the real world, and it has reached alarming proportions on social media as well. These days our lives have become increasingly reliant on social media platforms, such as Facebook. This is due to the rapid advancement of technology and communication. In Bangladesh, the number of people using social media platforms is also rapidly increasing. In English, detecting hate speech on social media is a difficult task. Comparatively, Bengali is a complicated language with few datasets available. As a result, detection of Bengali hate speech becomes even more challenging. In this paper, we present a new dataset of 10,133 user comments. We have collected them from the comment section of various public Facebook pages. We explore the performance of various machine learning and deep learning models in detecting hate speech. Bengali pre-trained word embeddings from fastText are used to train the models. We are especially interested in Convolutional Neural Network (CNN). To our knowledge it was never used for hate speech detection in binary classification. Another goal of this research is to create a new and large dataset, which will facilitate further research of Bengali Hate Speech Detection. All machine learning and deep learning models performed very well from our experiments. But, Support Vector Machine (SVM) is the one that performed the best among them.

Over the last two decades, social media platforms have grown dramatically. Twitter and Facebook are the two most popular social media platforms, with millions of active users posting billions of messages daily. These platforms allow users to have freedom of expression. However, some users exploit this facility by disseminating hate speeches. Manual detection and censorship of such hate speeches are impractical; thus, an automatic detection mechanism is required to detect and counter hate speeches in a real-time environment. Most research in hate speech detection has been carried out in the English language. Still, minimal work has been explored in other languages, mainly Urdu written in Roman Urdu script. A few research have attempted machine learning, and deep learning models for Roman Urdu hate speech detection; however, due to a scarcity of Roman Urdu resources, and a large corpus with defined annotation rules, a robust hate speech detection model is still required. With this motivation, this study contributes in the following manner: we developed annotation guidelines for Roman Urdu Hate Speech. Second, we constructed a new Roman Urdu Hate Speech Dataset (RU-HSD-30K) that was annotated by a team of experts using the annotation rules. To the best of our knowledge, the Bi-LSTM model with an attention layer for Roman-Urdu Hate Speech Detection has not been explored. Therefore, we developed a context-aware Roman Urdu Hate Speech detection model based on Bi-LSTM with an attention layer and used custom word2vec for word embeddings. Finally, we examined the effect of lexical normalization of Roman Urdu words on the performance of the proposed model. Different traditional as well as deep learning models, including LSTM and CNN models, were used as baseline models. The performance of the models was assessed in terms of evaluation matrices like accuracy, precision, recall, and F1-score. The generalization of each model is also evaluated on a cross-domain dataset. Experimental results revealed that Bi-LSTM with attention outperformed the traditional machine learning models and other deep learning models with an accuracy score of 0.875 and an F-Score of 0.885. In addition, the results demonstrated that our suggested model (Bi-LSTM with Attention Layer) is more general than previous models when applied to unseen data. The results confirmed that lexical normalization of Roman Urdu words enhanced the performance of the suggested model.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside DL architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints.

In an open source software development environment, it is hard to decide the number of group members required for resolving software issues. Developers generally reply to issues based totally on their domain knowledge and interest, and there are no predetermined groups. The developers openly collaborate on resolving the issues based on many factors, such as their interest, domain expertise, and availability. This study compares eight different algorithms employing machine learning and deep learning, namely—Convolutional Neural Network, Multilayer Perceptron, Classification and Regression Trees, Generalized Linear Model, Bayesian Additive Regression Trees, Gaussian Process, Random Forest and Conditional Inference Tree for predicting group size in five open source software projects developed and managed using an open source development framework GitHub. The social information foraging model has also been extended to predict group size in software issues, and its results compared to those obtained using machine learning and deep learning algorithms. The prediction results suggest that deep learning and machine learning models predict better than the extended social information foraging model, while the best-ranked model is a deep multilayer perceptron((R.M.S.E. sequelize—1.21, opencv—1.17, bitcoin—1.05, aseprite—1.01, electron—1.16). Also it was observed that issue labels helped improve the prediction performance of the machine learning and deep learning models. The prediction results of these models have been used to build an Issue Group Recommendation System as an Internet of Things application that recommends and alerts additional developers to help resolve an open issue.

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology

Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy.

In recent times, Bangladesh has experienced significant political changes, sparking debates and discussions across various platforms, particularly on social media. As political opinions continue to proliferate, the rise of political hate speech targeting political groups and leaders has become increasingly evident. Such hate speech often includes undemocratic language or incitements to violence, which can destabilize social harmony. This study focuses on detecting political hate speech in Bangla text, providing valuable insights for social media moderation and public sentiment analysis. It highlights the challenges of hate speech detection in low-resource languages (LRLs) like Bangla, particularly in the context of recent political unrest. To tackle this issue, we introduced a novel dataset, BanglaPoliHaS-D, specifically designed for identifying political hate speech in Bangla. We explored a range of machine learning (ML), deep learning (DL), and transformer-based approaches and the results turned out to be quite promising. While most ML, DL, and transformer-based models demonstrated strong performance, Bangla-BERT outperformed all other approaches, attaining the highest accuracy of 92.41%.

Hate speech is plaguing the cyberspace along with user-generated content. Adding counter speech has become an effective way to combat hate speech online. Existing datasets and models target either (a) hate speech or (b) hate and counter speech but disregard the context. This paper investigates the role of context in the annotation and detection of online hate and counter speech, where context is defined as the preceding comment in a conversation thread. We created a context-aware dataset for a 3-way classification task on Reddit comments: hate speech, counter speech, or neutral. Our analyses indicate that context is critical to identify hate and counter speech: human judgments change for most comments depending on whether we show annotators the context. A linguistic analysis draws insights into the language people use to express hate and counter speech. Experimental results show that neural networks obtain significantly better results if context is taken into account. We also present qualitative error analyses shedding light into (a) when and why context is beneficial and (b) the remaining errors made by our best model when context is taken into account.

Research and development have demonstrated that effective building energy prediction is significant for enhancing energy efficiency and ensuring grid reliability. Many machine learning (ML) models, particularly deep learning (DL) approaches, are widely used for power or peak demand forecasting. However, evaluating prediction models solely based on accuracy is insufficient, as complex models often suffer from low interpretability and high computational costs, making them difficult to implement in real-world applications. This study proposes a multi-perspective evaluation analysis that includes prediction accuracy (both overall and at different power levels), interpretability (global/local perspectives and model structure), and computational efficiency. Three popular DL models—recurrent neural network, gated recurrent unit, long short-term memory, and three tree-based models—random forecast, extreme gradient boosting, and light gradient boosting machine—are analyzed due to their popularity and high prediction accuracy in the field of power demand prediction. The comparison reveals the following: (1) The best-performing prediction model changes under different power demand levels. In scenarios with lower power usage patterns, tree-based models achieve an average CV-RMSE of 13.62%, which is comparable to the 12.17% average CV-RMSE of DL models. (2) Global and local interpretations indicate that past power use and time-related features are the most important. Tree-based models excel at identifying which specific lagged features are more significant. (3) The DL model behavior can be interpreted by visualizing the hidden state at each layer to reveal how the model captures temporal dynamics across different time steps. However, tree-based models are more intuitive to interpret using straightforward decision rules and structures. This study provides guidance for applying ML algorithms to load forecasting, offering multiple perspectives on model selection trade-offs.

The internet has become an everyday tool to communicate and network with people around the globe, but its perceived anonymity, availability, and instant access have made it an environment conducive to spreading hateful content and connecting to like-minded individuals with similar hateful ideologies. Hate speech and other prejudice-motivated behavior, however, need to be considered on a continuum of victimization, and "like other social processes, [be seen as] dynamic and in a state of constant movement and change, rather than static and fixed" (Bowling, 1993, p. 238). It is a social process that is marked by multiple, repeat, and constant victimization (Bowling, 1993), with victims no longer distinguishing between specific hateful events, and rather normalizing experiences of hateful conduct "as an everyday, unwanted but routine reality of being 'different'" (Chakraborti, 2016, p. 581). Understanding hateful behavior and victimization as a process allows us to connect "low-level" incidents of hateful behavior to the more serious and life-threatening incidents at the more extreme end of the spectrum (Bowling & Phillips, 2002). The Christchurch attacks in New Zealand and their link to hateful communication on the online platform 8chan is only one such example of how online hate speech and cyberhate can escalate to "in real life" attacks, leaving the online sphere and spilling into the offline world. As per Allport's (1954) scale of prejudice, more extreme forms of prejudice-motivated violence are founded on "lower level" acts of prejudice and bias, therefore, hateful content online should not be ignored. Intervening online to interrupt or counter hateful behavior already at the lower end of the scale of prejudice becomes important; online interventions which are to be identified and synthesized through this systematic review. Allport's (1954) scale of prejudice will be the basis for this systematic review. Early on, Allport (1954) asserted that individuals with negative attitudes toward groups are likely to act out on these prejudices "somehow, somewhere" (p. 14), and that the more intense such negative attitudes are, the more hostile the action will be. Allport (1954) put forward a scale of acts of prejudice to illustrate different degrees of acting out negative attitudes, a scale that starts with antilocution (or what we call hate speech), described as explicitly expressing prejudices through negative verbal remarks to either friends or strangers (Allport, 1954). Avoidance is the next level on the scale of prejudice, with people avoiding members of certain groups, followed by discrimination, where distinctions are made between people based on prejudices, which leads to the active exclusion of members from certain groups (Allport, 1954). This level of acting on prejudices is routed in institutional or systemic prejudices, for example, in the differential treatment of people within employment or education practices, but also within the criminal justice system, or through social exclusion of certain minority group members. Physical attack is the next level on the scale of prejudice, which includes violence against members of certain groups by physically acting on negative attitudes or prejudices. The last level is extermination, which is the ultimate act of violence against members of specific groups, an expression of prejudice that systematically eradicates an entire group of people (e.g., genocide or lynchings; Allport, 1954). Allport's (1954) scale of prejudice makes it clear how hate speech/cyberhate is connected to more extreme forms of violence motivated by specific prejudices and biases, with hate speech (or antilocutions) being only the starting point on a 5-point continuum (Bilewicz & Soral, 2020). The importance of this scale of prejudice is not only that it clearly illustrates a range of different ways and intensity levels to act out prejudices, but also the "progression from verbal aggression to physical violence or, in other words, the performative potential of hate speech" (Allport, 1954; Kopytowska & Baider, 2017, p. 138). This is where interventions at the lower level of the scale of prejudices, interventions targeting hate speech/cyberhate, become important. There is no universal definition of hateful conduct online, but there is some consensus that hate speech targets disadvantaged social groups (Jacobs & Potter, 1998). Bakalis (2018) more narrowly defines cyberhate as "any use of technology to express hatred towards a person or persons because of a protected characteristic—namely race, religion, gender, sexual orientation, disability and transgender identity" (p. 87). Another definition that also points out the ambiguity and challenges involved with identifying more subtle forms of hate speech, and also making reference to the potential threat of hate speech escalating to offline violence, is that put forward by Fortuna and Nunes (2018), who analyzed various definitions of hate speech "Hate speech is language that attacks or diminishes, that incites violence or hate against groups, based on specific characteristics such as physical appearance, religion, descent, national or ethnic origin, sexual orientation, gender identity or other, and it can occur with different linguistic styles, even in subtle forms or when humour is used" (p. 5). In this systematic review, we also distinguish hate speech/cyberhate specifically from other forms of harmful online activity, such as cyber-bullying, harassment, trolling or flaming, as perpetrators of such online behavior repeatedly and systematically target specific individuals to cause upset, to seek out negative reactions, or to create discord on the internet. In contrast, hate speech/cyberhate is more general and does not necessarily target a specific individual (Al-Hassan & Al-Dossari, 2019), instead hate speech/cyberhate heavily features prejudice, bias and intolerance toward certain groups within society. With the majority of hate speech happening online, interventions that take place online are an important way to challenge prejudice and bias, potentially reaching masses of people across the globe. The unique feature of the internet is that such individual negative attitudes toward minority groups and more extreme hateful ideology can find its way onto certain platforms and can instantly connect people sharing similar prejudices. By closing the social and spatial distance, the internet creates a form of collective identity (Perry, 2000, p. 123) and can convince individuals with even the most extreme ideologies that others out there share their views (Gerstenfeld et al., 2003). In addition, the enormous frequency of hate speech/cyberhate within online environments creates a sense of normativity to hatred and the potential for acts of intergroup violence or political radicalization (Bilewicz & Soral, 2020, p. 9). It is, therefore, important to challenge this hate speech epidemic (Bilewicz & Soral, 2020), especially since hate movements have increasingly crossed into the mainstream (Perry, 2000). With hate speech/cyberhate posing a threat to the social order by violating social norms (Soral et al., 2018), perceptions of social norms as either supporting or opposing prejudice has been found to have an influence on how individuals react online (Hsueh et al., 2015). Seeing other people post prejudiced (opposed to antiprejudiced) comments online can lead to the adoption of an online group's biases and can influence an individual's own perceptions and feelings toward the targeted stigmatized group (Hsueh et al., 2015). In addition, research around desensitization also suggests that being exposed to hate speech leads to desensitization, which further leads to an increase in outgroup prejudice toward groups targeted by such speech (Soral et al., 2018). With society increasingly recognizing that it is inappropriate to express prejudices in public settings, many interventions will include some form of social norms nudging to reduce such prejudices; interventions that "nudge behavior in the desired direction" (Titley et al., 2014, p. 60). Therefore, hate speech not only affects minority group members, but also has an influence on opinions of majority group members (Soral et al., 2018), which makes strategies that can elicit change in people's prejudice-related attitudes crucial (see, e.g., Zitek & Hebl, 2007). Governments around the world face increased demand for understanding and countering hateful ideology and violent extremism both online and offline (e.g., the Christchurch Call in New Zealand). The U.S. Government's 2011 CVE Strategy highlights the importance of ongoing research and analysis, the sharing of knowledge and best practices internationally, and the countering of hateful ideologies and propaganda (see also Department of Homeland Security, 2016, 2019). The goal of this systematic review is to use an integrated and interdisciplinary approach to examine the effectiveness of online campaigns and strategies for reducing hate speech and cyberhate. The internet also provides an opportunity to reach masses of people who have been exposed to hateful content and ideology online, therefore, this systematic review will focus on online interventions addressing online hate speech and cyberhate. The specific settings where we would expect to see the online interventions deployed will be on websites, text messaging applications, and online and social media platforms including, but not limited to, Facebook, Instagram, TikTok, WhatsApp, Google, YouTube, and Snapchat. As mentioned previously, many online interventions will be based on social norm nudges to reduce online hate. These interventions aim to change people's online behavior and encourage individuals or groups to conform to established social norms. The communication of social norms can happen through establishing community standards on online platforms themselves (e.g., Facebook, Twitter, etc.), through more formal online training courses, or through anti-hate speech/anti-cyberhate campaigns teaching people to recognize hate, embrace diversity, and stand up to bias. Such prevention campaigns are designed to challenge bias and build ally behaviors by supplying people with constructive responses to combat, for example, antisemitism racism, and homophobia, as well as provide resources to help people explore and critically reflect on current events. Other interventions may add messages to hateful online comments, counter hateful content or extremist ideology, or redirect people to more credible sources. Both peers and parents have been found to foster racial consciousness and identity development, define interracial relationships and cultivate ethnic heritage and culture (Hagerman, 2016). Socialization influences how children understand their group's social position and their membership within that group by providing an understanding of racial, religious, and sexual privilege (Bowman & Howard, 1985). Socialization often reflects peers' and parents' experiences with racism, discrimination, and their ideological perspectives about race, religion, or sexuality (Umaña-Taylor & Fine, 2004). This is important because peers and parents who feel discriminated against or believe that the "other" is a threat may impart their prejudices to their children or friends, which could lead them to interpret the social world with similar discriminatory views and/or behavior. Individuals who feel socially alienated or rejected are especially vulnerable to such socialization practices as they feel that adopting these views will provide them with a sense of acceptance and belonging (Leiken, 2012). Regardless of how an individual develops certain racial, religious, or sexual biases, the online interventions under review are expected to target and reduce the production of original hateful content such as antisemitic Tweets and/or homophobic blog posts as well as the consumption of hate speech material (e.g., watching or reading hate speech videos or blogs). For example, some interventions take a rather broad messaging approach by implementing racial sensitivity and diversity training through Public Service Announcements, peer-to-peer dialogue workshops, or films that provide opportunities for youth and adults to self-reflect and learn about historical oppression, people of color, women, and the LGBTQIA+ community from credible sources. The factual understanding of diverse groups is often supplemented by experiences with people within the group. These educational programs often identify a cultural guide who is willing to introduce youth to new experiences and who can aid in processing thoughts, feelings, and behaviors. These interventions intend to dispute and contradict negative stereotypes associated with specific cultures, people, and institutions by sharing different points of view based on human rights values such as openness, respect for difference, freedom, and equality (Gomes, 2017). Moreover, such interventions tend to involve blanket bans on specific behaviors enforced through the public promotion of norms or individual sanctions enforced by moderators. Other interventions, such as the "Redirect Method," are narrower in their messaging. These interventions generate curated playlists and collections of authentic content that challenge hate speech/cyberhate narratives and propaganda (Helmus & Klein, 2018). For instance, people who are directly searching for extremist content online may be linked to videos and written content that confronts such claims. These videos are designed to be objective in appearance instead of containing material that explicitly counters extremist propaganda. The underlying goal of this type of interventions is to provide credible content that effectively undermines extremist messaging but does not overtly attack the source of propaganda. In addition to confronting hate speech narratives, these interventions provide users with links to numerous social services such as anger management training, drug and alcohol treatment, and mental health resources. Online platforms, such as Twitter and Facebook, have also started to employ a similar method, redirecting people who comment on or share "fake news" or conspiracy theories, which often are fraught with prejudicial undertones and are harmful to minority groups, to more credible content and news sources. The aforementioned interventions are designed to counter-balance these biased perceptions (e.g., unsupported claims of the Black community as criminal or the LGBTQIA+ community as pathologized) Blacks as criminals, LGBTQIA+ as pathologized) by blunting the occurrence of racist discourse and reducing the likelihood these individuals will internalize and normalize racial, religious, and/or sexual prejudices (Qian et al., 2019). Being in new situations is uncomfortable and often awakens fears and apprehensions that can block our experiential development. Acquiring information or being exposed to minority-run businesses, poverty, and writings from minority authors allows a person to understand the thoughts, hopes, fears, and aspirations of the people outside their racial perspective rather than from the perspective of the majority society (Dunham et al., 2013; Lee et al., 2017). Doing so, counters racist programming by challenging hegemonic beliefs, which can lead to the acceptance of tolerant attitudes and the reduction of hateful expressions online. Findings from the proposed review will enhance our understanding of the effectiveness of online anti-hate speech/anti-hate interventions, will help ensure that programming funds are dedicated to the most-effective efforts, and will play a critical role in helping individual programs improve the quality of service provisions. It will inform governments and policymakers of the current state of such online efforts, what works and which modes of interventions to implement, and help guide economically viable investments in nation-state security. Our search of the scholarly literature identified one review, Blaya (2019), as similar to the proposed topic. Blaya's (2019) review, however, focused on the prevalence, type, and characteristics of existing interventions for counteracting cyberhate and did not include a meta-analysis. Two other similar reviews focused on exposure to extremist online content (Hassan et al., 2018) and communication channels associated with cyber-racism (Bliuc et al., 2018). A search of the Campbell Library using key terms (hate OR radical*) returned two protocols and one review identified for further inspection to assess potential overlap. The protocols include "Psychosocial processes and intervention strategies behind Islamist deradicalization: A scoping review" by de Carvalho et al. (2019) and "Police programs that seek to increase community connectedness for reducing violent extremism behavior, attitudes and beliefs" by Mazerolle et al. (2020). A further review on a similar topic is a recently completed Campbell review (January 2020), "Counter-narratives for the prevention of violent radicalization: A systematic review of targeted interventions" by Carthy et al. (2018) at the National University of Ireland, Galway. Our proposed review is distinguished from the de Carvalho et al. (2019) review in that we are focusing on hate speech and cyberhate generally without delimiting our approach to a specific type of radicalization (e.g., Islamist). Furthermore, we are electing to complete a systematic review and meta-analysis. Likewise, the protocol by Mazerolle et al. (2020) focuses on interventions involving police officers either as initiators, recipients, or implementers of community connectedness interventions. Our review will focus specifically on any online intervention, which may or may not involve police, but police will not be the focus nor be the basis of the online intervention strategy. Judging from Carthy et al. (2018) protocol, we anticipate our review will also capture counter-narrative interventions, but will differ based on setting, timing, and scope of interventions. Specifically, we are interested in online interventions that extend beyond counter-messaging campaigns to include a broad array of interventions outlined above and extend beyond radicalization to include everyday hate and prejudice. In addition to conducting a meta-analysis, the proposed review would build on Blaya's (2019) work by expanding the population parameters to include both adolescents as well as adults. Blaya (2019) limited her search to include interventions aimed toward youth, young people, children, young adults, adolescents, children, and teenagers and did not focus on extremism. The main objective of this review is to synthesize the available evidence on the effectiveness of online interventions aimed at reducing the creation and/or consumption of online hate speech/cyberhate material. To what extent are online interventions effective in reducing online hate speech/cyberhate? How is effectiveness related to the type of online hate speech/cyberhate intervention used? How is effectiveness related to the characteristics of individuals experiencing the online hate speech/cyberhate intervention (e.g., age, gender, race/ethnicity, offense history, childhood trauma)? Both experimental and quasi-experimental quantitative studies will be included. These study designs will address research questions #1 to #3. Eligible quantitative study designs include the following: Eligible experimental designs must involve random assignment of participants to distinct treatment and control group(s). Designs that involve quasi-random assignment of participants such as alternate case assignment are also eligible and will be coded as experimental designs. All eligible quasi-experimental designs must include a comparison group of participants compared to participants in the treatment condition. Eligible studies include those that report matching procedures (individual- or group-level) and statistical procedures employed to achieve equivalency between groups. Statistical procedures may but are not limited to, analysis, and Furthermore, in of a limited quantitative evidence we will also include quasi-experimental studies with comparison groups that provide of for both groups. will also be included. Eligible include designs with a control group and designs with or without a control group than quasi-experimental designs include studies that a comparison group of participants who either to in the study or who in a but out to the of a Eligible comparison include other online interventions or in which participants not or an online Both youth and participants of any gender sexual orientation, or will be eligible for this review. The eligible youth population will be study participants with a of through The eligible population will be study participants with a of and in which only a of the is eligible for example, a study in both online and offline hate speech be not anticipate studies based on as our will be and we will take to studies that only online interventions. will of the of a study for through and be we will elicit the of a the of and studies will be these studies will be they will be and be in the and any related Blaya's (2019) of intervention strategies to the potential of eligible interventions. The intervention is the of responses to hate speech/cyberhate, which includes the countering of violent extremism and to address online interventions that are eligible range from hateful content online specific (e.g., of social media to to online hate using targeted strategies (e.g., through hateful of studies focusing on online include the and of online and content online content and & 2018), hateful online comments to comments et al., 2018), and to users out of online are also interested in interventions such as the of 8chan this online platform linked to "in real life" attacks in New Zealand and the and interventions that further hateful online content and radicalization similar events. hateful content online such has up speech as well as around online users and hateful groups on to other online to hateful content online using targeted strategies therefore, been as an effective online include using the from & 2020), the use of to online responses to in online where hate speech has been (Qian et al., 2019), and redirecting online users to videos for example, Our systematic review will include a range of online interventions, many of which have only recently Two other strategies identified by Blaya (2019) are the and of hate speech/cyberhate using technology as well as the creation of online and These interventions include online counter-narrative the and/or use of online counter online interventions, online training, and online narrowly to address extremist ideologies and hate speech that incites targeted violence and In such interventions seek to or the occurrence of violent extremism or the of hate speech and extremist by channels and opportunities to such groups. The and intervention eligible for this systematic review educational programs for example, provide people with online and challenge 2019). will include online programs with an online (e.g., and and educational and online interventions. of these interventions may by individuals no longer in the creation and/or consumption of cyberhate and extremist material online. These online interventions may be by and internet service or or in the case of interventions. The comparison may be routine exposure and to hate speech/cyberhate or online The of is the creation and/or consumption of hateful content online. By we to the production and of original hateful content such as antisemitic racist and/or homophobic blog The consumption of hate speech material may include or being a of a hate watching or reading hate speech videos or being a target of online hate speech/cyberhate, or hate speech material. of include and of study participants such as and attitudes toward hate Eligible studies must report a or (or to be included. There will be no exclusion on the source of for the and can be from any institutional or completed by will include any of from strategies to increase the scale of of potentially effective anti-hate speech and interventions for These could include to or to the creation of and behaviors. can also include such as a of hate speech/cyberhate to other platforms instead of a reduction of hate All described in eligible studies will be in the will focus on the between and the current The starting with the when the internet to a and community et al., are for an approach in the lower end of our search to the may be it is hate speech/cyberhate online through or and some studies may capture Our population of studies will also be limited to studies in and but of studies completed in any as we are focused on online content that can be and across and nation-state The language parameters reflect the language of the review Our will where studies the of study in will be between the members of the review These will be and as a from the protocol in the review. In the of a change in we will search online OR OR internet OR Twitter OR OR 8chan OR OR OR OR OR OR OR OR OR OR speech" OR cyberhate OR OR OR OR OR speech OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR peer-to-peer OR OR OR

A kidney tumor is a dangerous disease that affects mostly males more than females, with a high mortality rate. The kidneys help to maintain fluid and solute balance in the body by filtering waste materials. A kidney tumor is categorized into three types: benign, malignant and indolent. Renal Cell Carcinoma (RCC) is a form of malignant tumor caused by defective genes. RCC classification is essential for further treatment planning. Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound are some of the imaging modalities used to diagnose kidney tumors. Ultrasound is a screening technology that differentiates between solid and cystic lesions. However, cross-sectional imaging systems such as CT and MRI are also essential for the detection of kidney tumors. Mostly, CT is used for the identification of tumors. The goal of this review paper is to evaluate Deep Learning (DL) and Machine Learning (ML) models for detecting kidney tumors. Initially, existing review articles are evaluated to analyze previous techniques. After that, it offers a thorough examination of ML and DL models along with their datasets and limitations. Furthermore, this paper analyzes segmentation and classification-based techniques for detecting kidney tumors. The analysis of limitations of DL, ML, segmentation and classification techniques is utilized to highlight the challenges. Afterwards, different types of datasets are analyzed in this review paper to predict the best dataset. The limitations in this review paper help to construct a novel model for the detection of kidney tumors.

Small and Medium-sized Enterprises (SMEs) in Sri Lanka are increasingly turning to social media to gain insights into consumer preferences; however, they frequently lack sophisticated analytical tools to derive actionable insights. This research introduces a hybrid deep learning framework aimed at sentiment and emotion analysis specifically for the Sri Lankan cake sector. Customer reviews and social media comments were gathered and processed through various methods including text cleaning, emoji translation, stopword elimination, tokenization, and sequence padding. Several deep learning models— Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Units (GRU) were assessed, along with hybrid models that combined CNN and BiLSTM with GPT-2 embeddings. The CNN+GPT-2 model achieved the highest accuracy in sentiment classification at 96.14%, while BiLSTM performed best in emotion classification with an accuracy of 91.57%. In terms of trend forecasting, Long Short-Term Memory (LSTM) outshone both SARIMA and Prophet, yielding the lowest error metrics and the most reliable sentiment predictions. These results underscore the potential of hybrid deep learning and transformer-based models to effectively capture complex consumer sentiments and provide SMEs with practical, AI-driven insights. This study is among the first to implement such hybrid NLP models within a localized Sri Lankan context, presenting a scalable approach for business intelligence and informed decision-making.