Unsupervised keyword extraction from microblog posts via hashtags

© River Publishers. Nowadays, huge amounts of texts are being generated for social networking purposes on Web. Keyword extraction from such texts like microblog posts benefits many applications such as advertising, search, and content filtering. Unlike traditional web pages, a microblog post usually has some special social feature like a hashtag that is topical in nature and generated by users. Extracting keywords related to hashtags can reflect the intents of users and thus provides us better understanding on post content. In this paper, we propose a novel unsupervised keyword extraction approach for microblog posts by treating hashtags as topical indicators. Our approach consists of two hashtag enhanced algorithms. One is a topic model algorithm that infers topic distributions biased to hashtags on a collection of microblog posts. The words are ranked by their average topic probabilities. Our topic model algorithm can not only find the topics of a collection, but also extract hashtag-related keywords. The other is a random walk based algorithm. It first builds a word-post weighted graph by taking into account posts themselves. Then, a hashtag biased random walk is applied on this graph, which guides the algorithm to extract keywords according to hashtag topics. Last, the final ranking score of a word is determined by the stationary probability after a number of iterations. We evaluate our proposed approach on a collection of real Chinese microblog posts. Experiments show that our approach is more effective in terms of precision than traditional approaches considering no hashtag. The result achieved by the combination of two algorithms performs even better than each individual algorithm

소셜 미디어 속 루머 탐지를 위한 그래프 합성곱 신경망과 어텐션 메커니즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. 김종권.Social media has been a great disseminator for new information and thoughts. Due to its accessibility of sharing information, however, social media has also become an ideal platform for propagations of rumors, fake news, and misinformation. Rumors on social media not only mislead the users of online but also affects the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Previous learning-based rumor detection methods adopted to use contents, users, or propagation features of rumors. However, the methods are limited to represent rumor propagation as static graphs, which arent optimal for capturing the dynamic information of the rumors. In this study, we propose a novel graph convolutional networks with attention mechanism model named, Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on two real-world datasets demonstrate that our model, Dynamic GCN, achieves superior results over the state-of-the-art models in the rumor detection task.소셜 미디어는 강력한 정보 전달력을 가진 매체로 새로운 정보와 생각의 전파 창구이다. 소셜 미디어의 특징인 접근성은 때론 루머, 가짜 뉴스, 잘못된 정보의 전파에서도 이상적인 플랫폼이 된다. 소셜 미디어 속 루머는 온라인 사용자를 오도할 뿐만 아니라 때론 현실 세계에도 큰 영향을 미친다. 따라서, 루머를 탐지하고 그 전파를 막는 노력이 요구된다. 기존의 루머 탐지 방법은 루머의 내용, 사용자, 또는 전파 과정의 정보를 특성으로 이용한다. 이러한 방법은 루머의 전파를 정적 그래프로 표현하며 그 구조적 특성을 이용한다. 하지만 이는 루머의 동적 특성을 포착하지 못한다는 한계를 가지고 있다. 본 연구에서는 그래프 합성곱 신경망 (graph convolutional networks: GCN)과 어텐션 메커니즘 (attention mechanism)을 활용한 동적 그래프 합성곱 신경망 (Dynamic GCN) 루머 탐지 모델을 제시한다. 먼저, 소셜 미디어 속 루머 게시글들 (posts) 과 그의 답장이 되는 글들(responsive posts)을 이용하여 루머의 전파 과정을 정적 그래프로 표현한다. 시간 정보를 통해 전파 과정을 포함하고 있는 정적 그래프의 집합인 그래프 스냅숏 (graph snapshot) 시퀀스 (sequence)를 만들게 된다. 어텐션 메커니즘을 활용한 그래프 스냅숏 표현 학습은 루머 전파의 구조적 시간적 정보를 모두 효과적으로 반영한다. 실제 트위터 데이터를 이용한 실험을 통하여 제시된 모델의 성능이 다른 비교 모델들보다 높음을 확인할 수 있었다.Chapter I Introduction 1 Chapter II Related Work 5 2.1 Rumor Detection 5 2.2 Graph Convolutional Networks 6 2.3 Learning Sequences & Attention Mechanism 7 Chapter III Problem Definition 9 Chapter IV Dynamic GCN with Attention Mechanism 11 4.1 Snapshot Generation 13 4.2 Graph Convolutional Networks 14 4.3 Readout Layer 15 4.4 Attention Mechanism 16 4.5 Training & Prediction 17 Chapter V Experiments 18 5.1 Datasets 18 5.2 Baselines 20 5.3 Experimental Setup & Implementation Details 21 5.4 Performance Evaluations 24 5.5 Ablation Study 25 Chapter VI Conclusion 30 Bibliography 31 초 록 40Maste

NEEDMINING: IDENTIFYING MICRO BLOG DATA CONTAINING CUSTOMER NEEDS

The design of new products and services starts with the identification of needs of potential customers or users. Many existing methods like observations, surveys, and experiments draw upon specific efforts to elicit unsatisfied needs from individuals. At the same time, a huge amount of user-generated content in micro blogs is freely accessible at no cost. While this information is already analyzed to monitor sentiments towards existing offerings, it has not yet been tapped for the elicitation of needs. In this paper, we lay an important foundation for this endeavor: we propose a Machine Learning approach to identify those posts that do express needs. Our evaluation of tweets in the e-mobility domain demonstrates that the small share of relevant tweets can be identified with remarkable precision or recall results. Applied to huge data sets, the developed method should enable scalable need elicitation support for innovation managers—across thousands of users, and thus augment the service design tool set available to him

Stock market prediction using machine learning classifiers and social media, news

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble

ACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging

As key roles of online social networks, influential users in micro-blogging have the ability to influence the attitudes or behaviour of others. When it comes to marketing, the users’ influence should be associated with a certain topic or field on which people have different levels of preference and expertise. In order to identify and predict influential users in a specific topic more effectively, users’ actual influential capability on a certain topic and potential influence unlimited by topics is combined into a novel comprehensive framework named “ACQR” in this research. ACQR framework depicts the attributes of the influentials from four aspects, including activeness (A), centrality (C), quality of post (Q) and reputation (R). Based on this framework, a data mining method is developed for discovering and forecasting the top influentials. Empirical results reveal that our ACQR framework and the data mining method by TOPSIS and SVMs (with polynomial and RBF kernels) can perform very well in identifying and predicting influential users in a certain topic (such as iPhone 5). Furthermore, the dynamic change processes of users’ influence from longitudinal perspective are analysed and suggestions to the sales managers are provided

Predicting Influencer Virality on Twitter

The ability to successfully predict virality on Twitter holds great potential as a resource for Twitter influencers, enabling the development of more sophisticated strategies for audience engagement, audience monetization, and information sharing. To our knowledge, focusing exclusively on tweets posted by influencers is a novel context for studying Twitter virality. We find, among feature categories traditionally considered in the literature, that combining categories covering a range of information performs better than models only incorporating individual feature categories. Moreover, our general predictive model, encompassing a range of feature categories, achieves a prediction accuracy of 68% for influencer virality. We also investigate the role of influencer audiences in predicting virality, a topic we believe to be understudied in the literature. We suspect that incorporating audience information will allow us to better discriminate between virality classes, thus leading to better predictions. We pursue two different approaches, resulting in 10 different predictive models that leverage influencer audience information in addition to traditional feature categories. Both of our attempts to incorporate audience information plateau at an accuracy of approximately 61%, roughly a 7% decrease in performance compared to our general predictive model. We conclude that we are unable to find experimental evidence to support our claim that incorporating influencer audience information will improve virality predictions. Nonetheless, the performance of our general model holds promise for the deployment of a tool that allows influencers to reap the benefits of virality prediction. As stronger performance from the underlying model would make this tool more useful in practice to influencers, improving the predictive performance of our general model is a cornerstone of future work

Advances in Emotion Recognition: Link to Depressive Disorder

Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social text on social network platform. Recognition of emotion pattern based on explicit and implicit features extracted through wearable and other devices could be decoded through computational modeling. Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder. The chapter aims to summarize the main findings in the area of affective recognition and its applications in major depressive disorder (MDD), which have made rapid progress in the last decade