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PurposeThe COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly; it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible.Design/methodology/approachThe authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance.FindingsExperimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1–31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically.Originality/valueThe originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.

The topic model has been widely applied to various research domains such as information retrieval, data mining, and so on. It can discover topics of texts in an unsupervised way. In the early years, most researches mainly focused on long texts. With the emergence of the Internet, the number of short texts is growing rapidly. Most existing schemes to solve the sparsity problems of short texts, are mainly based on data aggregation or model improvements. Among them, the Biterm Topic Model is one of the most representative models. It proposed a new way to model topics based on document-level word pairs and has shown creativity and effectiveness. However, this strategy ignores those semantically similar and rarely co-occurrent word pairs. What’s more, most researches ignore the multi-sense phenomenon in natural languages. In this paper, we utilize multi-sense word vectors to extract similar word pairs from the whole corpus by considering multiple senses. Based on this idea, we introduce a novel short-text topic model, which disambiguates multiple senses of words and generates more reasonable global biterms. Experimental results on two open-source English datasets have shown superiority to state-of-the-art topic models.

Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis With the development of online social media, people prefer to express their opinions and discuss the latest news online We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online In this paper, we take Sina Weibo, the most widely used online social media in China, for analysis and experiments We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler, including texts and images posted by users In order to effectively discover what is being discussed about COVID-19 without human labeling, we propose a unified multi-modal framework, including an unsupervised short-text topic model to discover and track bursty topics, and a self-supervised model to learn image features so that we can retrieve related images about COVID-19 Experimental results have shown the effectiveness and superiority of the proposed models, and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19

Fine-grained sentiment analysis is a useful tool for producers to understand consumers’ needs as well as complaints about products and related aspects from online platforms. In this article, we define a novel task named “Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA)”. It investigates the sentiment towards entities and their related aspects. It makes the well-studied aspect-based sentiment analysis a special case of this type, where the number of entities is limited to one. We contribute a new dataset for this task, with multi-entity Chinese posts in it. We propose to model context, entity, and aspect memory to address the task and incorporate dependency information for further improvement. Experiments show that our methods perform significantly better than baseline methods on datasets for both ME-ABSA task and ABSA task. The in-depth analysis further validates the effectiveness of our methods and shows that our methods are capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

Integral membrane proteins (MPs) make up a large proportion of the genomes of many organisms and the amount of sequenced proteins is growing at an unprecedented pace. As a result, MPs function pre-screen is of great necessity. To be functional, MPs must be expressed and localized through a series of elaborate sub-cellular processes. It is worth noting that subtle changes in sequence may lead to drastic changes in expression and localization. In light of the above observation, taking the complementary information of sequence-based proteins into consideration, a novel boosting Dual-Modal Dual-Label (DMDL) algorithm with hypothesis reuse is proposed. On the one hand, DMDL treats sequence features and structure features as dual-modal. On the other hand, DMDL considers eukaryotic expression and plasma membrane localization as dual-label, which would be of great value as a pre-screen for MPs function. Meanwhile, two label sets interact, which can not only make the best use of two modal features, but also could effectively exploit the relationship between two label sets. An assessment of real-world channelrhodopsins (ChRs) chimeras clearly validate the effectiveness of DMDL algorithm compared with state-of-the-art algorithms. In addition, extensive experiments are performed on adapted public datasets, showing effeceiveness of hypothesis reuse mechanism in DMDL.

Transfer learning has made great achievements in many fields and many excellent algorithms have been proposed. In recent years, many scholars have focused on a new research area called online transfer learning, which is different from general transfer learning. Online transfer learning concentrates on how to build a good classifier on the target domain when the training data arrive in an online/sequential manner. This paper focuses on online transfer learning problem based on a single source domain under homogeneous space. The existing algorithms HomOTL-I and HomOTL-II simply ensemble the classifiers on the source and target domains directly. When the distribution difference between the source domain and the target domain is large, it will not result in a good transfer effect. We are inspired by the idea of the boosting algorithm, that is we could form a strong classification model by a combination of multiple weak classifications. We train multiple classifiers on the source domain in an offline manner using AdaBoost algorithm, combine these classifiers on source domain with the classifier trained in an online manner on the target domain to form multiple weak combination in an ensemble manner. Based on the above ideas, we propose two algorithms AB-HomOTL-I and AB-HomOTLII, which have different ways to adjust the weights. We tested our algorithms on sentiment analysis dataset and 20newsgroup dataset. The results show that our algorithms are superior to other baseline algorithms

In our daily life, short texts have been everywhere especially since the emergence of social network. There are countless short texts in online media like twitter, online Q&A sites and so on. Discovering topics is quite valuable in various application domains such as content recommendation and text characterization. Traditional topic models like LDA are widely applied for sorts of tasks, but when it comes to short text scenario, these models may get stuck due to the lack of words. Recently, a popular model named BTM uses word co-occurrence relationship to solve the sparsity problem and is proved effectively. However, both BTM and extended models ignore the inside relationship between words. From our perspectives, more related words should appear in the same topic. Based on this idea, we propose a model named RIBS-TM which makes use of RNN for relationship learning and IDF for filtering high-frequency words. Experiments on two real-world short text datasets show great utility of our model.

In our daily life, anomalies are everywhere in various application domains. Most anomalies may cause huge losses if we fail to detect them in advance. A lot of researches on this field have been carried out for years so as to detect anomalies as soon as possible. Among them, machine learning is one of the most used techniques. Previous work tries to improve detection by choosing various classifiers, which has achieved some success. But few has considered the different losses each anomaly might cause. As we know, anomalies with higher significance will cause higher losses. In this paper, we aim to minimize the losses by proposing an improved cost-sensitive GBDT algorithm named LF-GBDT. LF-GBDT is designed to optimize a self-defined loss function. Experiments with both traditional classification algorithms such as CART, Adaboost etc. And cost-sensitive algorithms such as MetaCost, CSC show that our method can both improve the detection of important anomalies and reduce the total losses.