text processing

Vegetarianism is one of the trends that has received a lot of feedback on social networks. The content published by users reflects their feelings and opinions towards this trend and its various aspects. In this regard, a dataset containing more than sixty thousand tweets published in 2023 about vegetarianism was collected. This dataset was used to extract user sentiment towards different aspects of vegetarianism. First, a method based on RoBERTa language model was proposed to analyze the implicit sentiment hidden in tweets. Then, using the Latent Dirichlet Allocation topic modeling approach, some relevant aspects and topics related to vegetarianism were extracted. In the next step, a method based on DeBERTa language model was used to analyze tweet sentiment towards different aspects that had been extracted. Various frequency and sentiment distribution charts for different aspects in the field of vegetarianism were examined. The results of emotional analysis based on RoBERTa and DeBERTa models were compared side by side. Data analysis using the DeBERTa model showed that users had mostly tweeted positive sentiments regarding the plant and lifestyle aspects. However, for the Animal aspect, most tweets were negative. For both Diet and Company aspects, most tweets were positive or neutral with values close to each other. During the discussion, some implicit knowledge related to this topic was also examined.

In the last decade, with the expansion of the World Wide Web, the speed and ease of access to ideas, documents, articles, manuscripts, and data collected by others has increased. This has made the exchange of information and ideas between researchers and producers of science easier, but on the other hand, it has made it easier to apply unauthorized copies, write summaries without mentioning the source, and steal literary texts in general. Since universities and educational centers make scientific and research resources easily available to most users, recognizing the authenticity of scientific texts in these centers is more important and, of course, more sensitive. In this research, a method is presented to compare the related parts using the blocking of document parts. In the proposed method, after classifying the documents into two categories of main documents and suspicious documents, preprocessing has been done with the aim of eliminating word stops and new wording. Then the documents are segmented and using cosine similarity, the degree of similarity of the texts with each other is determined. The proposed method in the test of 50 documents in the data set has an accuracy of 94%, which is an improvement of 2% compared to one of the similar methods.

The rumor is a collective attempt to interpret a vague but attractive situation by using the power of words. Therefore, identifying the rumor language can be helpful in identifying it. The previous research has focused more on the contextual information to reply tweets and less on the content features of the original rumor to address the rumor detection problem. Most of the studies have been in the English language, but more limited work has been done in the Persian language to detect rumors. This study analyzed the content of the original rumor and introduced informative content features to early identify Persian rumors (i.e., when it is published on news media but has not yet spread on social media) on Twitter and Telegram. Therefore, the proposed model is based on physical and non-physical content features in three categories including, lexical, syntactic, and pragmatic. These features are a combination of the common content features along with the proposed new content-based features. Since no social context information is available at the time of posting rumors, the proposed model is independent of propagation-based features and relies on the content-based information of the original rumor. Although in the proposed model, much information (including user information, the userchr('39')s reaction to the rumor, and propagation structures) are ignored, but helpful content information can be obtained for classification by content analysis of the original rumor.Several experiments have been performed on the various combinations of feature sets (i.e., common and proposed content features) to explore the capability of features in distinguishing rumors and non-rumors separately and jointly. To this end, three machine learning algorithms including, Random Forest (RF), AdaBoost, and Support Vector Machine (SVM) have been used as strong classifications to evaluate the accuracy of the proposed model. To achieve the best performance of classification algorithms on the training dataset, it is necessary to use feature selection techniques. In this study, the Sequential Forward Floating Search (SFFS) approach has been used to select valuable features. Also, the statistical results of the t-test on the P-value (<=0.05) demonstrate that most of the new features proposed in this study reveal statistically significant differences between rumor and non-rumor documents. The experimental results are shown the performance of new proposed features to improve the accuracy of the rumor detection. The F-measure of the proposed model to detect Persian rumors on the Twitter dataset was 0.848, on the Kermanshah earthquake dataset was 0.952 and on the Telegram dataset was 0.867, which indicated the ability of the proposed method to identify rumors only by focusing on the content features of the original rumor text. The results of evaluating the proposed model on Twitter rumors show that, despite the short length of Twitter tweets and the extraction of limited content information from tweets, the proposed model can detect Twitter rumors with acceptable accuracy. Hence, the ability of content features to distinguish rumors from non-rumors is proven.