Fine-grained Emotion Role Detection Based on Retweet Information

User behaviors in online social networks convey not only literal information but also one’s emotion attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this paper, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model, named as MERM, to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

VIRAL TOPIC PREDICTION AND DESCRIPTION IN MICROBLOG SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH

تطوير منهجية تعتمد على تنقيب الأنماط المتكررة المرنة للكشف عن الأحداث الهامة في المدونات العربية المصغرة

Recently, Microblogs have become the new communication medium between users. It allows millions of users to post and share content of their own activities, opinions about different topics. Posting about occurring real-world events has attracted people to follow events through microblogs instead of mainstream media. As a result, there is an urgent need to detect events from microblogs so that users can identify events quickly, also and more importantly to aid higher authorities to respond faster to occurring events by taking proper actions. While considerable researches have been conducted for event detection on the English language. Arabic context have not received much research even though there are millions of Arabic users. Also existing approaches rely on platform dependent features such as hashtags, mentions, retweets etc. which make their approaches fail when these features are not present in the process. In addition to that, approaches that depend on the presence of frequently used words only do not always detect real events because it cannot differentiate events and general viral topics. In this thesis, we propose an approach for Arabic event detection from microblogs. We first collect the data, then a preprocessing step is applied to enhance the data quality and reduce noise. The sentence text is analyzed and the part-of-speech tags are identified. Then a set of rules are used to extract event indicator keywords called event triggers. The frequency of each event triggers is calculated, where event triggers that have frequencies higher than the average are kept, or removed otherwise. We detect events by clustering similar event triggers together. An Adapted soft frequent pattern mining is applied to the remaining event triggers for clustering. We used a dataset called Evetar to evaluate the proposed approach. The dataset contains tweets that cover different types of Arabic events that occurred in a one month period. We split the dataset into different subsets using different time intervals, so that we can mimic the streaming behavior of microblogs. We used precision, recall and fmeasure as evaluation metrics. The highest average f-measure value achieved was 0.717. Our results were acceptable compared to three popular approaches applied to the same dataset.حديثا،ً أصبحت المدونات الصغيرة وسيلة إتصال جديدة بين المستخدمين. فقد سمحت لملايين المستخدمين من نشر ومشاركة محتويات متعلقة بأنشطتهم وأرائهم عن مواضيع مختلفة. إن نشر المحتوى المتعلق بالأحداث الجارية في العالم الحقيقي قد جذب الناس لمتابعة الأحداث من خلال المدونات الصغيرة بدلاً من وسائل الإعلام الرئيسية. نتيجة لذلك، أصبحت هناك حاجة طارئة لكشف الأحداث من الدونات الصغيرة حتى يتمكن المستخدمون من تحديد الأحداث الجارية بشكل أسرع، أيضا والأهم من ذلك، مساعدة السلطات العليا للإستجابة بشكل سريع في عمل اللازم عند حدوث حدثا ما. في حين أنه أجريت العديد من الأبحاث على كشف الأحداث باللغة الإنجليزية، إلا أن السياق العربي لم يأخذ نصيبا وفير ا في هذا المجال، على الرغم من وجود الملايين من المستخدمين العرب. ايضا،ً العديد من المناهج الموجودة حاليا تعتمد على خصائص معتمدة على المنصة المستخدمة في البحث مثل وسم الهاشتاق، وتأشيرة المستخدم، وإعادة التغريد، إلخ. مما يجعل النهج المستخدم يتأثر سلبا في حال لم تكن هذه الخصائص موجودة أثناء عملية الكشف عن الأحداث. بالإضافة الي ذلك، المناهج التي تعتمد فقط على وجود الكلمات الأكثر استخداما لا تكشف الاحداث الحقيقية دائما لانها لا تستطيع التفرقة بين الحدث والمواضيع العامة الشائعة. في هذه الأطروحة، نقترح نهج لكشف الأحداث العربية من المدونات الصغيرة. أولاً نقوم بجمع البيانات، ثم نقوم بتجهيزها من خلال تحسينها وتقليل الشوائب فيها. يتم تحليل نص الجملة لإستخراج الأوسمة الخاصة بأجزاء الكلام. بعدها نقوم بتطبيق مجموعة من القواعد لإستخراج الكلمات الدلالية التي تشير إلي الأحدات و تسمى مشغلات الأحداث. يتم حساب عدد تكرار كل مشغل حدث، بحيث يتم الإحتفاظ على المشغلات التي لها عدد تكراراكبر من المتوسط ويتم حذف عكس ذالك. يتم الكشف عن الحدث من خلال تجميع مشغلات الأحداث المتشابهة مع بعضها. حيث نقوم بتطبيق إصدار ملائم من خوارزمية "التنقيب الناعم عن الأنماط المتكررة" على مشغلات الأحداث التي تبقت لكي يتم تجميع المتشابه منها. قمنا بإستخدام قاعدة بيانات تسمى (Evetar) لتقييم النهج المقترح. حيث تحتوي قاعدة البيانات على تغريدات تغطى عدة انواع من الأحداث العربية التي حدثت خلال فترة شهر. لكي نقوم بمحاكاة طريقة تدفق البيانات في المدونات الصغيرة، قمنا بتقسييم البيانات إلي عدة مجموعات بناءاُ على فترات زمنية مختلفة. تم استخدام كل من (Precision)، (Recall)، (F-Measure) كمقياس للتقييم، حيث كانت أعلى متوسط قيمة لل (F-Measure) تم الحصول عليها هي 0.717 . تعتبر النتائج التي حصلنا عليها مقبولة مقارنة مع ثلاث مناهج مشهورة تم تطبيقها على نفس قاعدة البيانات

Diffusion of Lexical Change in Social Media

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity -- especially with regard to race -- plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.Comment: preprint of PLOS-ONE paper from November 2014; PLoS ONE 9(11) e11311

A Content Analysis of Chinese Weibo Posts About 2019-20 Hong Kong Demonstrations

The author investigated the framing process of the 2019-20 Hong Kong protest movement and the prevalence of news frames identified in earlier studies on political framing. The characteristics of each message were also investigated. The author content analyzed 502 Weibo posts collected from three Chinese state-owned media: People’s Daily (人民日报), CCTV (央视新闻), and Global Times (环球时报). According to this study’s findings, certain events in the period surrounding the 2019-20 Hong Kong demonstrations followed a four-step framing process: diagnostic framing, prognostic framing, motivational framing and framing of results. The results also showed that, overall, the law and order, crime and justice frame occurred most frequently, followed by the morality, cultural identity, security and defense and quality of life frame respectively. Posts with high engagement tended to embrace the following characteristics: short and concise content, strong symbolic representations, low-risk mobilization, presence of strong emotional components and visualization elements.Master of Art

ANALYZING IMAGE TWEETS IN MICROBLOGS

Ph.DDOCTOR OF PHILOSOPH

Visual Analytics Methods for Exploring Geographically Networked Phenomena

abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models. Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Automated Assessment of the Aftermath of Typhoons Using Social Media Texts

Disasters are one of the major threats to economics and human societies, causing substantial losses of human lives, properties and infrastructures. It has been our persistent endeavors to understand, prevent and reduce such disasters, and the popularization of social media is offering new opportunities to enhance disaster management in a crowd-sourcing approach. However, social media data is also characterized by its undue brevity, intense noise, and informality of language. The existing literature has not completely addressed these disadvantages, otherwise vast manual efforts are devoted to tackling these problems. The major focus of this research is on constructing a holistic framework to exploit social media data in typhoon damage assessment. The scope of this research covers data collection, relevance classification, location extraction and damage assessment while assorted approaches are utilized to overcome the disadvantages of social media data. Moreover, a semi-supervised or unsupervised approach is prioritized in forming the framework to minimize manual intervention. In data collection, query expansion strategy is adopted to optimize the search recall of typhoon-relevant information retrieval. Multiple filtering strategies are developed to screen the keywords and maintain the relevance to search topics in the keyword updates. A classifier based on a convolutional neural network is presented for relevance classification, with hashtags and word clusters as extra input channels to augment the information. In location extraction, a model is constructed by integrating Bidirectional Long Short-Time Memory and Conditional Random Fields. Feature noise correction layers and label smoothing are leveraged to handle the noisy training data. Finally, a multi-instance multi-label classifier identifies the damage relations in four categories, and the damage categories of a message are integrated with the damage descriptions score to obtain damage severity score for the message. A case study is conducted to verify the effectiveness of the framework. The outcomes indicate that the approaches and models developed in this study significantly improve in the classification of social media texts especially under the framework of semi-supervised or unsupervised learning. Moreover, the results of damage assessment from social media data are remarkably consistent with the official statistics, which demonstrates the practicality of the proposed damage scoring scheme