Temporal Feedback for Tweet Search with Non-Parametric Density Estimation

This paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do relevant documents tend to cluster together in time? We explore this question in the context of tweet search and temporal feedback: starting with an initial set of results from a baseline retrieval model, we estimate the temporal density of relevant documents, which is then used for result reranking. Our contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating this information into a standard retrieval model. Experiments on TREC datasets confirm that our temporal feedback formulation improves search effectiveness, thus providing support for our hypothesis. Our approach outperforms both a standard baseline and previous temporal retrieval models. Temporal feedback improves over standard lexical feedback (with and without human judgments), illustrating that temporal relevance signals exist independently of document content

Modeling Temporal Evidence from External Collections

Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.Comment: To appear in WSDM 201

Exploring the topical structure of short text through probability models : from tasks to fundamentals

Recent technological advances have radically changed the way we communicate. Today’s communication has become ubiquitous and it has fostered the need for information that is easier to create, spread and consume. As a consequence, we have experienced the shortening of text messages in mediums ranging from electronic mailing, instant messaging to microblogging. Moreover, the ubiquity and fast-paced nature of these mediums have promoted their use for unthinkable tasks. For instance, reporting real-world events was classically carried out by news reporters, but, nowadays, most interesting events are first disclosed on social networks like Twitter by eyewitness through short text messages. As a result, the exploitation of the thematic content in short text has captured the interest of both research and industry. Topic models are a type of probability models that have traditionally been used to explore this thematic content, a.k.a. topics, in regular text. Most popular topic models fall into the sub-class of LVMs (Latent Variable Models), which include several latent variables at the corpus, document and word levels to summarise the topics at each level. However, classical LVM-based topic models struggle to learn semantically meaningful topics in short text because the lack of co-occurring words within a document hampers the estimation of the local latent variables at the document level. To overcome this limitation, pooling and hierarchical Bayesian strategies that leverage on contextual information have been essential to improve the quality of topics in short text. In this thesis, we study the problem of learning semantically meaningful and predictive representations of text in two distinct phases: • In the first phase, Part I, we investigate the use of LVM-based topic models for the specific task of event detection in Twitter. In this situation, the use of contextual information to pool tweets together comes naturally. Thus, we first extend an existing clustering algorithm for event detection to use the topics learned from pooled tweets. Then, we propose a probability model that integrates topic modelling and clustering to enable the flow of information between both components. • In the second phase, Part II and Part III, we challenge the use of local latent variables in LVMs, specially when the context of short messages is not available. First of all, we study the evaluation of the generalization capabilities of LVMs like PFA (Poisson Factor Analysis) and propose unbiased estimation methods to approximate it. With the most accurate method, we compare the generalization of chordal models without latent variables to that of PFA topic models in short and regular text collections. In summary, we demonstrate that by integrating clustering and topic modelling, the performance of event detection techniques in Twitter is improved due to the interaction between both components. Moreover, we develop several unbiased likelihood estimation methods for assessing the generalization of PFA and we empirically validate their accuracy in different document collections. Finally, we show that we can learn chordal models without latent variables in text through Chordalysis, and that they can be a competitive alternative to classical topic models, specially in short text.Els avenços tecnològics han canviat radicalment la forma que ens comuniquem. Avui en dia, la comunicació és ubiqua, la qual cosa fomenta l’ús de informació fàcil de crear, difondre i consumir. Com a resultat, hem experimentat l’escurçament dels missatges de text en diferents medis de comunicació, des del correu electrònic, a la missatgeria instantània, al microblogging. A més de la ubiqüitat, la naturalesa accelerada d’aquests medis ha promogut el seu ús per tasques fins ara inimaginables. Per exemple, el relat d’esdeveniments era clàssicament dut a terme per periodistes a peu de carrer, però, en l’actualitat, el successos més interessants es publiquen directament en xarxes socials com Twitter a través de missatges curts. Conseqüentment, l’explotació de la informació temàtica del text curt ha atret l'interès tant de la recerca com de la indústria. Els models temàtics (o topic models) són un tipus de models de probabilitat que tradicionalment s’han utilitzat per explotar la informació temàtica en documents de text. Els models més populars pertanyen al subgrup de models amb variables latents, els quals incorporen varies variables a nivell de corpus, document i paraula amb la finalitat de descriure el contingut temàtic a cada nivell. Tanmateix, aquests models tenen dificultats per aprendre la semàntica en documents curts degut a la manca de coocurrència en les paraules d’un mateix document, la qual cosa impedeix una correcta estimació de les variables locals. Per tal de solucionar aquesta limitació, l’agregació de missatges segons el context i l’ús d’estratègies jeràrquiques Bayesianes són essencials per millorar la qualitat dels temes apresos. En aquesta tesi, estudiem en dos fases el problema d’aprenentatge d’estructures semàntiques i predictives en documents de text: En la primera fase, Part I, investiguem l’ús de models temàtics amb variables latents per la detecció d’esdeveniments a Twitter. En aquest escenari, l’ús del context per agregar tweets sorgeix de forma natural. Per això, primer estenem un algorisme de clustering per detectar esdeveniments a partir dels temes apresos en els tweets agregats. I seguidament, proposem un nou model de probabilitat que integra el model temàtic i el de clustering per tal que la informació flueixi entre ambdós components. En la segona fase, Part II i Part III, qüestionem l’ús de variables latents locals en models per a text curt sense context. Primer de tot, estudiem com avaluar la capacitat de generalització d’un model amb variables latents com el PFA (Poisson Factor Analysis) a través del càlcul de la likelihood. Atès que aquest càlcul és computacionalment intractable, proposem diferents mètodes d estimació. Amb el mètode més acurat, comparem la generalització de models chordals sense variables latents amb la del models PFA, tant en text curt com estàndard. En resum, demostrem que integrant clustering i models temàtics, el rendiment de les tècniques de detecció d’esdeveniments a Twitter millora degut a la interacció entre ambdós components. A més a més, desenvolupem diferents mètodes d’estimació per avaluar la capacitat generalizadora dels models PFA i validem empíricament la seva exactitud en diverses col·leccions de text. Finalment, mostrem que podem aprendre models chordals sense variables latents en text a través de Chordalysis i que aquests models poden ser una bona alternativa als models temàtics clàssics, especialment en text curt.Postprint (published version

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

Towards trust-aware recommendations in social networks

Recommender systems have been strongly researched within the last decade. With the emergence and popularization of social networks a new fi eld has been opened for social recommendations. Introducing new concepts such as trust and considering the network topology are some of the new strategies that recommender systems have to take into account in order to adapt their techniques to these new scenarios. In this thesis a simple model for recommendations on twitter is developed to apply some of the known techniques and explore how well the state of the art does in a real scenario. The thesis can serve as a basis for further social recommender system research