Locally embedded presages of global network bursts

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially non-bursting network state is not fully understood. In this study, we develop a new state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during non-bursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean field activity for predicting future global bursts. Moreover, the inter-cell variability in the burst predictability is found to reflect the network structure realized in the non-bursting periods. These findings demonstrate the deterministic mechanisms underlying the locally concentrated early-warnings of the global state transition in self-organized networks

Predicting Information Diffusion on Social Media

Sotsiaalmeedia on saanud moodsa elu osaks. Pidevalt tekib juurde informatsiooni, mida maailmaga jagatakse. Informatsiooni hajumist on varasemalt uuritud paljude teadlaste poolt, kuna sel on rakendusi erinevates valdkondades, nagu näiteks sotsiaalmeediaturundamine ja uudiste levimise uurimine. Informatsiooni leviku kiirust mõjutab selle olulisus inimestele. Käesolevas töös uuritakse info hajumist sotsiaalvõrgustikus ja ennustatakse sisu populaarsust kasutades juhendatud masinõppe algoritme. Kolme Twitterist pärit andmestikku analüüsitakse ja kasutatakse erinevate masinõppe mudelite konstrueerimiseks.Defineerisime säutsu populaarsuse kui taaspostituste arvu, mida iga originaalsäuts sai, ning püstitasime uurimisprobleemid binaarsete ja mitmeklassiliste ennustusülesannetena. Uurisime, kuidas esialgne säutsude taaspostitamise käitumine mõjutab mudelite ennustusvõimekust. Lisaks analüüsisime, kas viimase tunni taaspostituskäitumine aitab ennustada taas-postituskäitumist järgneva tunni jooksul. Täiendav tähelepanu oli suunatud ka ennustuseks tähtsate tunnuste leidmiseks.Binaarse ennustuse puhul näitasid mudelid tulemusi AUC (area under curve) kuni 95% ning F1-skoori kuni 87%. Mitmeklassiliste ennustuste puhul suutsid mudelid saavutada kuni 60% üldise täpsuse ning F1-skoori kuni 67%. Paremad ennustustäpsused saavutati siis, kui postitustel olid väga madalad või väga kõrged taaspostituste arvud. Me genereerisime mudelid kasutades üht andmestikku ning testisime neid ülejäänud kahe peal. See näitas, et mudelid on piisavalt robustsed, et tegeleda erinevate teemadega.Social media has become a part of the everyday life of modern society. A lot of infor-mation is created and shared with the world continuously. Predicting information has been studied in the past by many researchers since it has its applications in various domains such as viral marketing, news propagation etc.Some information spreads faster compared to others depending on what interests people. In this thesis, by using supervised machine learning algorithms, we studied information diffusion in a social network and predicted content popularity. Three datasets from Twitter are collected and analysed for building and testing various models based on different ma-chine learning algorithms.We defined tweet popularity as number of retweets any original message received and stated our research problems as binary and multiclass prediction tasks. We investigated how initial retweeting behaviour of a message affects the predictive power of a model. We also analysed if a recent one-hour retweeting behaviour can help to predict a tweet popu-larity of the following hour. Besides that, main focus is made on finding features im-portant for the prediction.For binary prediction, the models showed performance of AUC up to 95% and F1 up to 87%. For multiclass prediction, the models were able to predict up to 60% of overall accu-racy and 67% of F1, with more accurate performance of classes with messages with very low and high retweet counts comparing to others. We created our models using one da-taset and tested our approach on the other two datasets, which showed that the models are robust enough to deal with multiple topics

Social media analytics with applications in disaster management and COVID-19 events

Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled methods that can provide appropriate tags or labels for further analysis for timely situation-awareness. In that direction, this work proposes machine learning models to identify the people who are seeking assistance using social media during a disaster and further demonstrates a prototype application that can collect and process Twitter data in real-time, identify the stranded people, and create rescue scheduling. In addition, to understand the people’s reactions to different trending topics, this work proposes a unique auxiliary feature-based deep learning model with adversarial sample generation for emotion detection using tweets related to COVID-19. This work also presents a custom Q&A-based RoBERTa model for extracting related phrases for emotions. Finally, with the aim of polarization detection, this research work proposes a deep learning pipeline for political ideology detection leveraging the tweet texts and the expressed emotions in the text. This work also studies and conducts the historical emotion and polarization analysis of the COVID-19 pandemic in the USA and several individual states using tweeter data --Abstract, page iv

Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository

Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection