Temporal Information Models for Real-Time Microblog Search

Real-time search in Twitter and other social media services is often biased towards the most recent results due to the “in the moment” nature of topic trends and their ephemeral relevance to users and media in general. However, “in the moment”, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where itwas shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches: (1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time; (2) efficient methods for federated query expansion towards the improvement of query meaning; and (3) exploiting multiple sources towards the detection of temporal query intent. It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step

Event detection on streams of short texts for decision-making

L'objectif de cette thèse est de concevoir d'évènements sur les réseaux sociaux permettant d'assister les personnes en charge de prises de décisions dans des contextes industriels. Le but est de créer un système de détection d'évènement permettant de détecter des évènements à la fois ciblés, propres à des domaines particuliers mais aussi des évènements généraux. En particulier, nous nous intéressons à l'application de ce système aux chaînes d'approvisionnements et plus particulièrement celles liées aux matières premières. Le défi est de mettre en place un tel système de détection, mais aussi de déterminer quels sont les évènements potentiellement impactant dans ces contextes. Cette synthèse résume les différentes étapes des recherches menées pour répondre à ces problématiques. Architecture d'un système de détection d'évènements Dans un premier temps, nous introduisons les différents éléments nécessaires à la constitution d'un système de détection d'évènements. Ces systèmes sont classiquement constitués d'une étape de filtrage et de nettoyage des données, permettant de s'assurer de la qualité des données traitées par le reste du système. Ensuite, ces données sont représentées de manière à pouvoir être regroupées par similarité. Une fois ces regroupements de données établis, ils sont analysés de manière à savoir si les documents les constituants traitent d'un évènement ou non. Finalement, l'évolution dans le temps de ces évènements est suivie. Nous avons proposé au cours de cette thèse d'étudier les problématiques propres à chacune de ces étapes. Représentation textuelles de documents issus des réseaux sociaux Nous avons comparé différentes méthodes de représentations des données textuelles, dans le contexte de notre système de détection d'évènements. Nous avons comparé les performances de notre système de détection à l'algorithme First Story Detection (FSD), un algorithme ayant les mêmes objectifs. Nous avons d'abord démontré que le système que nous proposons est plus performant que le FSD, mais aussi que les architectures récentes de réseaux de neurones (transformeur) sont plus performantes que TF-IDF dans notre contexte, contrairement à ce qui avait été montré dans le contexte du FSD. Nous avons ensuite proposé de combiner différentes représentations textuelles afin d'exploiter conjointement leurs forces. Détection d'évènement, suivi et évaluation Nous avons proposé des approches pour les composantes d'analyse de regroupement de documents ainsi que pour le suivi de l'évolution de ces évènements. En particulier, nous utilisons l'entropie et la diversité d'utilisateurs introduits dans [Rajouter les citations] pour évaluer les regroupements. Nous suivons ensuite leur évolution au cours du temps en faisant des comparaisons entre regroupements à des instants différents, afin de créer des chaînes de regroupements. Enfin, nous avons étudié comment évaluer des systèmes de détection d'évènements dans des contextes où seulement peu de données annotées par des humains sont disponibles. Nous avons proposé une méthode permettant d'évaluer automatiquement les systèmes de détection d'évènement en exploitant des données partiellement annotées. Application au contexte des matières premières. Afin de spécifier les types d'évènements à superviser, nous avons mené une étude historique des évènements ayant impacté le cours des matières premières. En particulier, nous nous sommes focalisé sur le phosphate, une matière première stratégique. Nous avons étudié les différents facteurs ayant une influence, proposé une méthode reproductible pouvant être appliquée à d'autres matières premières ou d'autres domaines. Enfin, nous avons dressé une liste d'éléments à superviser pour permettre aux experts d'anticiper les variations des cours.The objective of this thesis is to design an event detection system on social networks to assist people in charge of decision-making in industrial contexts. The event detection system must be able to detect both targeted, domain-specific events and general events. In particular, we are interested in the application of this system to supply chains and more specifically those related to raw materials. The challenge is to build such a detection system, but also to determine which events are potentially influencing the raw materials supply chains. This synthesis summarizes the different stages of research conducted to answer these problems. Architecture of an event detection system First, we introduce the different building blocks of an event detection system. These systems are classically composed of a data filtering and cleaning step, ensuring the quality of the data processed by the system. Then, these data are embedded in such a way that they can be clustered by similarity. Once these data clusters are created, they are analyzed in order to know if the documents constituting them discuss an event or not. Finally, the evolution of these events is tracked. In this thesis, we have proposed to study the problems specific to each of these steps. Textual representation of documents from social networks We compared different text representation models, in the context of our event detection system. We also compared the performances of our event detection system to the First Story Detection (FSD) algorithm, an algorithm with the same objectives. We first demonstrated that our proposed system performs better than FSD, but also that recent neural network architectures perform better than TF-IDF in our context, contrary to what was shown in the context of FSD. We then proposed to combine different textual representations in order to jointly exploit their strengths. Event detection, monitoring, and evaluation We have proposed different approaches for event detection and event tracking. In particular, we use the entropy and user diversity introduced in ... to evaluate the clusters. We then track their evolution over time by making comparisons between clusters at different times, in order to create chains of clusters. Finally, we studied how to evaluate event detection systems in contexts where only few human-annotated data are available. We proposed a method to automatically evaluate event detection systems by exploiting partially annotated data. Application to the commodities context In order to specify the types of events to supervise, we conducted a historical study of events that have impacted the price of raw materials. In particular, we focused on phosphate, a strategic raw material. We studied the different factors having an influence, proposed a reproducible method that can be applied to other raw materials or other fields. Finally, we drew up a list of elements to supervise to enable experts to anticipate price variations

Temporal Context Modeling for Text Streams

There is increasing recognition that time plays an essential role in many information seeking tasks. This dissertation explores temporal models on evolving streams of text and the role that such models play in improving information access. I consider two cases: a stream of social media posts by many users for tweet search and a stream of queries by an individual user for voice search. My work explores the relationship between temporal models and context models: for tweet search, the evolution of an event serves as the context of clustering relevant tweets; for voice search, the user's history of queries provides the context for helping understand her true information need. First, I tackle the tweet search problem by modeling the temporal contexts of the underlying collection. The intuition is that an information need in Twitter usually correlates with a breaking news event, thus tweets posted during that event are more likely to be relevant. I explore techniques to model two different types of temporal signals: pseudo trend and query trend. The pseudo trend is estimated through the distribution of timestamps from an initial list of retrieved documents given a query, which I model through continuous hidden Markov approach as well as neural network-based methods for relevance ranking and sequence modeling. As an alternative, the query trend, is directly estimated from the temporal statistics of query terms, obviating the need for an initial retrieval. I propose two different approaches to exploit query trends: a linear feature-based ranking model and a regression-based model that recover the distribution of relevant documents directly from query trends. Extensive experiments on standard Twitter collections demonstrate the superior effectivenesses of my proposed techniques. Second, I introduce the novel problem of voice search on an entertainment platform, where users interact with a voice-enabled remote controller through voice requests to search for TV programs. Such queries range from specific program navigation (i.e., watch a movie) to requests with vague intents and even queries that have nothing to do with watching TV. I present successively richer neural network architectures to tackle this challenge based on two key insights: The first is that session context can be exploited to disambiguate queries and recover from ASR errors, which I operationalize with hierarchical recurrent neural networks. The second insight is that query understanding requires evidence integration across multiple related tasks, which I identify as program prediction, intent classification, and query tagging. I present a novel multi-task neural architecture that jointly learns to accomplish all three tasks. The first model, already deployed in production, serves millions of queries daily with an improved customer experience. The multi-task learning model is evaluated on carefully-controlled laboratory experiments, which demonstrates further gains in effectiveness and increased system capabilities. This work now serves as the core technology in Comcast Xfinity X1 entertainment platform, which won an Emmy award in 2017 for the technical contribution in advancing television technologies. This dissertation presents families of techniques for modeling temporal information as contexts to assist applications with streaming inputs, such as tweet search and voice search. My models not only establish the state-of-the-art effectivenesses on many related tasks, but also reveal insights of how various temporal patterns could impact real information-seeking processes

Context-Aware Message-Level Rumour Detection with Weak Supervision

Social media has become the main source of all sorts of information beyond a communication medium. Its intrinsic nature can allow a continuous and massive flow of misinformation to make a severe impact worldwide. In particular, rumours emerge unexpectedly and spread quickly. It is challenging to track down their origins and stop their propagation. One of the most ideal solutions to this is to identify rumour-mongering messages as early as possible, which is commonly referred to as "Early Rumour Detection (ERD)". This dissertation focuses on researching ERD on social media by exploiting weak supervision and contextual information. Weak supervision is a branch of ML where noisy and less precise sources (e.g. data patterns) are leveraged to learn limited high-quality labelled data (Ratner et al., 2017). This is intended to reduce the cost and increase the efficiency of the hand-labelling of large-scale data. This thesis aims to study whether identifying rumours before they go viral is possible and develop an architecture for ERD at individual post level. To this end, it first explores major bottlenecks of current ERD. It also uncovers a research gap between system design and its applications in the real world, which have received less attention from the research community of ERD. One bottleneck is limited labelled data. Weakly supervised methods to augment limited labelled training data for ERD are introduced. The other bottleneck is enormous amounts of noisy data. A framework unifying burst detection based on temporal signals and burst summarisation is investigated to identify potential rumours (i.e. input to rumour detection models) by filtering out uninformative messages. Finally, a novel method which jointly learns rumour sources and their contexts (i.e. conversational threads) for ERD is proposed. An extensive evaluation setting for ERD systems is also introduced

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

End-to-end Neural Information Retrieval

In recent years we have witnessed many successes of neural networks in the information retrieval community with lots of labeled data. Yet it remains unknown whether the same techniques can be easily adapted to search social media posts where the text is much shorter. In addition, we find that most neural information retrieval models are compared against weak baselines. In this thesis, we build an end-to-end neural information retrieval system using two toolkits: Anserini and MatchZoo. In addition, we also propose a novel neural model to capture the relevance of short and varied tweet text, named MP-HCNN. With the information retrieval toolkit Anserini, we build a reranking architecture based on various traditional information retrieval models (QL, QL+RM3, BM25, BM25+RM3), including a strong pseudo-relevance feedback baseline: RM3. With the neural network toolkit MatchZoo, we offer an empirical study of a number of popular neural network ranking models (DSSM, CDSSM, KNRM, DUET, DRMM). Experiments on datasets from the TREC Microblog Tracks and the TREC Robust Retrieval Track show that most existing neural network models cannot beat a simple language model baseline. How- ever, DRMM provides a significant improvement over the pseudo-relevance feedback baseline (BM25+RM3) on the Robust04 dataset and DUET, DRMM and MP-HCNN can provide significant improvements over the baseline (QL+RM3) on the microblog datasets. Further detailed analyses suggest that searching social media and searching news articles exhibit several different characteristics that require customized model design, shedding light on future directions