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2 pages, 161 KiB

Open AccessEditorial

Special Issue on Applications of Artificial Intelligence on Social Media

by Huan Wang and Wen Zhang

Appl. Sci. 2023, 13(21), 11662; https://doi.org/10.3390/app132111662 - 25 Oct 2023

Viewed by 2135

Abstract

The explosive expansion of social media platforms across the globe, including the likes of TikTok, WeChat, Twitter, and Facebook, has ushered in an era of unparalleled possibilities for individuals to forge connections and chronicle their social engagements [...] Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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21 pages, 6499 KiB

Open AccessArticle

Intelligent Information System for Product Promotion in Internet Market

by Khrystyna Lipianina-Honcharenko, Carsten Wolff, Anatoliy Sachenko, Oksana Desyatnyuk, Svitlana Sachenko and Ivan Kit

Appl. Sci. 2023, 13(17), 9585; https://doi.org/10.3390/app13179585 - 24 Aug 2023

Cited by 4 | Viewed by 1896

Abstract

The influence of Internet marketing has grown so much that producers must now reconfigure their businesses from offline operation to online presence simply to meet user expectations. Thus, the development of an intelligent information system for product promotion online is quite relevant. It [...] Read more.

The influence of Internet marketing has grown so much that producers must now reconfigure their businesses from offline operation to online presence simply to meet user expectations. Thus, the development of an intelligent information system for product promotion online is quite relevant. It may lead to automatized selection of competing products and advertising content, a subsequent increase in the effectiveness of advertisements, and a decrease in costs for Internet ad placements. The paper presents the approach for creating an intelligent information system for product promotion in online spaces that makes it possible to reduce advertising costs. A methodology is based on outcomes of own previous studies as well as the flow nature and semantics of data streams. The framework of the proposed intelligent system includes the four key procedures and functions: intelligent formation of keywords for advertising content based on feedback, intelligent formation of product catalogs of online stores, generation of advertising content, and generation of improved advertising content and its targeting generation of text based on keywords. An experimental study confirmed that the effectiveness of posts on social media increased by at least 125%, while the price decreased by 87%. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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12 pages, 615 KiB

Open AccessArticle

External Slot Relationship Memory for Multi-Domain Dialogue State Tracking

by Xinlai Xing, Changmeng Yang, Dafei Lin, Da Teng, Panpan Chen and Xiaochuan Zhang

Appl. Sci. 2023, 13(15), 8943; https://doi.org/10.3390/app13158943 - 3 Aug 2023

Cited by 1 | Viewed by 1203

Abstract

Dialogue state tracking is an essential component in multi-domain dialogue systems that aims to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven [...] Read more.

Dialogue state tracking is an essential component in multi-domain dialogue systems that aims to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample often has limitations and may introduce noise and have high time complexity issues. In this paper, we propose an external slot relation memory-based dialogue state tracking model (ER-DST). By utilizing external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Our method is evaluated on MultiWOZ 2.0 and MultiWOZ 2.1, achieving improvements of 0.23% and 0.39% over the baseline models, respectively, while reducing the complexity of the slot relationship learning component to O(n). Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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17 pages, 1101 KiB

Open AccessArticle

SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction

by Haoyu Lu, Daofu Gong, Zhenyu Li, Feng Liu and Fenlin Liu

Appl. Sci. 2023, 13(9), 5341; https://doi.org/10.3390/app13095341 - 25 Apr 2023

Cited by 7 | Viewed by 2064

Abstract

Worries about the increasing number of Sybils in online social networks (OSNs) are amplified by a range of security issues; thus, Sybil detection has become an urgent real-world problem. Lightweight and limited data-friendly, LBP (Loopy Belief Propagation)-based Sybil-detection methods on the social graph [...] Read more.

Worries about the increasing number of Sybils in online social networks (OSNs) are amplified by a range of security issues; thus, Sybil detection has become an urgent real-world problem. Lightweight and limited data-friendly, LBP (Loopy Belief Propagation)-based Sybil-detection methods on the social graph are extensively adopted. However, existing LBP-based methods that do not utilize node attributes often assume a global or predefined homophily strength of edges in the social graph, while different user’s discrimination and preferences may vary, resulting in local homogeneity differences. Another issue is that the existing message-passing paradigm uses the same edge potential when propagating belief to both sides of a directed edge, which does not agree with the trust interaction in one-way social relationships. To bridge these gaps, we present SybilHP, a Sybil-detection method optimized for directed social networks with adaptive homophily prediction. Specifically, we incorporate an iteratively updated edge homophily estimation into the belief propagation to better adapt to the personal preferences of real-world social network users. Moreover, we endow message passing on edges with directionality by a direction-sensitive potential function design. As a result, SybilHP can better capture the local homophily and direction pattern in real-world social networks. Experiments show that SybilHP works with high detection accuracy on synthesized and real-world social graphs. Compared with various state-of-the-art graph-based methods on a large-scale Twitter dataset, SybilHP substantially outperforms existing methods. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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16 pages, 1073 KiB

Open AccessArticle

Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter

by Faisal S. Alsubaei

Appl. Sci. 2023, 13(5), 3013; https://doi.org/10.3390/app13053013 - 26 Feb 2023

Cited by 6 | Viewed by 3397

Abstract

It is obvious that one of the most significant challenges posed by Twitter is the proliferation of fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a result, the primary focus of this paper is on the identification [...] Read more.

It is obvious that one of the most significant challenges posed by Twitter is the proliferation of fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a result, the primary focus of this paper is on the identification of fraudulent accounts, fake information, and fake accounts on Twitter, in addition to the flow of content that these accounts post. The research utilized a design science methodological approach and developed a bot account referred to as “Fake Account Detector” that assists with the detection of inappropriate posts that are associated with fake accounts. To develop this detector, previously published tweets serve as the datasets for the training session. This data comes from Twitter and are obtained through the REST API. The technique of machine learning with random forest (RF) is then used to train the data. The high levels of accuracy (99.4%) obtained from the RF detection results served as the foundation for the development of the bot account. This detector tool, developed using this model, can be utilized by individuals, businesses, and government agencies to assist in the detection and prevention of Twitter problems related to fake news and fake accounts. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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22 pages, 2188 KiB

Open AccessArticle

An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud

by Sandhya Devi Gogula, Mohamed Rahouti, Suvarna Kumar Gogula, Anitha Jalamuri and Senthil Kumar Jagatheesaperumal

Appl. Sci. 2023, 13(2), 773; https://doi.org/10.3390/app13020773 - 5 Jan 2023

Cited by 8 | Viewed by 2896

Abstract

Sentiment analysis (SA), and emotion detection and recognition from text (EDRT) are recent areas of study that are closely related to each other. Sentiment analysis strives to identify and detect neutral, positive, or negative feelings from text. On the other hand, emotion analysis [...] Read more.

Sentiment analysis (SA), and emotion detection and recognition from text (EDRT) are recent areas of study that are closely related to each other. Sentiment analysis strives to identify and detect neutral, positive, or negative feelings from text. On the other hand, emotion analysis seeks to identify and distinguish types of feelings such as happiness, surprise, grief, disgust, fear, and anger through the expression of texts. We suggest a four-level strategy in this paper for recommending the best book to users. The levels include semantic network grouping of comparable sentences, sentiment analysis, reviewer clustering, and recommendation system. The semantic network groups comparable sentences at the first level utilizing pre-processed data from reviewer and book datasets using the parts of speech (POS) tagger. In order to extract keywords from the pre-processed data, feature extraction uses the bag of words (BOW) and term frequency-inverse document frequency (TF-IDF) approaches. SA is performed at the second level in two phases: training and testing, employing deep learning methodologies such as convolutional neural networks (CNN)-long short-term memory (LSTM). The results of this level are sent into the third level (clustering), which uses the clustering method to group the reviewers by age, location, and gender. In the last level, the model assessment is carried out with accuracy, precision, recall, sensitivity, specificity, G-mean, and F1-measure. The book suggestion system is designed to provide the highest level of accuracy within a minimum number of epochs when compared to the state-of-the methods, SVM, CNN, ANN, LSTM, and Bi-directional (BI)-LSTM. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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17 pages, 2030 KiB

Open AccessArticle

DPG-LSTM: An Enhanced LSTM Framework for Sentiment Analysis in Social Media Text Based on Dependency Parsing and GCN

by Zeyu Yin, Jinsong Shao, Muhammad Jawad Hussain, Yajie Hao, Yu Chen, Xuefeng Zhang and Li Wang

Appl. Sci. 2023, 13(1), 354; https://doi.org/10.3390/app13010354 - 27 Dec 2022

Cited by 8 | Viewed by 4017

Abstract

Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. [...] Read more.

Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. Despite earlier studies’ good performance, these methods lack syntactic information to guide feature development for contextual semantic linkages in social media text. In this paper, we introduce an enhanced LSTM-based on dependency parsing and a graph convolutional network (DPG-LSTM) for sentiment analysis. Our research aims to investigate the importance of syntactic information in the task of social media emotional processing. To fully utilize the semantic information of social media, we adopt a hybrid attention mechanism that combines dependency parsing to capture semantic contextual information. The hybrid attention mechanism redistributes higher attention scores to words with higher dependencies generated by dependency parsing. To validate the performance of the DPG-LSTM from different perspectives, experiments have been conducted on three tweet sentiment classification datasets, sentiment140, airline reviews, and self-driving car reviews with 1,604,510 tweets. The experimental results show that the proposed DPG-LSTM model outperforms the state-of-the-art model by 2.1% recall scores, 1.4% precision scores, and 1.8% F1 scores on sentiment140. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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16 pages, 522 KiB

Open AccessArticle

Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning

by Abdullah M. Alkadri, Abeer Elkorany and Cherry Ahmed

Appl. Sci. 2022, 12(22), 11388; https://doi.org/10.3390/app122211388 - 10 Nov 2022

Cited by 13 | Viewed by 2820

Abstract

In recent years, people have tended to use online social platforms, such as Twitter and Facebook, to communicate with families and friends, read the latest news, and discuss social issues. As a result, spam content can easily spread across them. Spam detection is [...] Read more.

In recent years, people have tended to use online social platforms, such as Twitter and Facebook, to communicate with families and friends, read the latest news, and discuss social issues. As a result, spam content can easily spread across them. Spam detection is considered one of the important tasks in text analysis. Previous spam detection research focused on English content, with less attention to other languages, such as Arabic, where labeled data are often hard to obtain. In this paper, an integrated framework for Twitter spam detection is proposed to overcome this problem. This framework integrates data augmentation, natural language processing, and supervised machine learning algorithms to overcome the problems of detection of Arabic spam on the Twitter platform. The word embedding technique is employed to augment the data using pre-trained word embedding vectors. Different machine learning techniques were applied, such as SVM, Naive Bayes, and Logistic Regression for spam detection. To prove the effectiveness of this model, a real-life data set for Arabic tweets have been collected and labeled. The results show that an overall improvement in the use of data augmentation increased the macro F1 score from 58% to 89%, with an overall accuracy of 92%, which outperform the current state of the art. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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18 pages, 2381 KiB

Open AccessArticle

Bot-MGAT: A Transfer Learning Model Based on a Multi-View Graph Attention Network to Detect Social Bots

by Eiman Alothali, Motamen Salih, Kadhim Hayawi and Hany Alashwal

Appl. Sci. 2022, 12(16), 8117; https://doi.org/10.3390/app12168117 - 13 Aug 2022

Cited by 10 | Viewed by 2486

Abstract

Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to [...] Read more.

Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph attention mechanism using a transfer learning (TL) approach, to predict social bots. We called the framework ‘Bot-MGAT’, which stands for bot multi-view graph attention network. The framework used both labeled and unlabeled data. We used profile features to reduce the overheads of the feature engineering. We executed our experiments on a recent benchmark dataset that included representative samples of social bots with graph structural information and profile features only. We applied cross-validation to avoid uncertainty in the model’s performance. Bot-MGAT was evaluated using graph SSL techniques: single graph attention networks (GAT), graph convolutional networks (GCN), and relational graph convolutional networks (RGCN). We compared Bot-MGAT to related work in the field of bot detection. The results of Bot-MGAT with TL outperformed, with an accuracy score of 97.8%, an F1 score of 0.9842, and an MCC score of 0.9481. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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19 pages, 3517 KiB

Open AccessArticle

Integroly: Automatic Knowledge Graph Population from Social Big Data in the Political Marketing Domain

by Héctor Hiram Guedea-Noriega and Francisco García-Sánchez

Appl. Sci. 2022, 12(16), 8116; https://doi.org/10.3390/app12168116 - 13 Aug 2022

Cited by 5 | Viewed by 2857

Abstract

Social media sites have become platforms for conversation and channels to share experiences and opinions, promoting public discourse. In particular, their use has increased in political topics, such as citizen participation, proselytism, or political discussions. Political marketing involves collecting, monitoring, processing, and analyzing [...] Read more.

Social media sites have become platforms for conversation and channels to share experiences and opinions, promoting public discourse. In particular, their use has increased in political topics, such as citizen participation, proselytism, or political discussions. Political marketing involves collecting, monitoring, processing, and analyzing large amounts of voters’ data. However, the extraction, integration, processing, and storage of these torrents of relevant data in the political domain is a very challenging endeavor. In the recent years, the semantic technologies as ontologies and knowledge graphs (KGs) have proven effective in supporting knowledge extraction and management, providing solutions in heterogeneous data sources integration and the complexity of finding meaningful relationships. This work focuses on providing an automated solution for the population of a political marketing-related KG from Spanish texts through Natural Language Processing (NLP) techniques. The aim of the proposed framework is to gather significant data from semi-structured and unstructured digital media sources to feed a KG previously defined sustained by an ontological model in the political marketing domain. Twitter and political news sites were used to test the usefulness of the automatic KG population approach. The resulting KG was evaluated through 18 quality requirements, which ensure the optimal integration of political knowledge. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence on Social Media)

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