A Network Model for Adaptive Information Retrieval

This thesis presents a network model which can be used to represent Associative Information Retrieval applications at a conceptual level. The model presents interesting characteristics of adaptability and it has been used to model both traditional and knowledge based Information Retrieval applications. Moreover, three different processing frameworks which can be used to implement the conceptual model are presented. They provide three different ways of using domain knowledge to adapt the user formulated query to the characteristics of a specific application domain using the domain knowledge stored in a sub-network. The advantages and drawbacks of these three adaptive retrieval strategies are pointed out and discussed. The thesis also reports the results of an experimental investigation into the effectiveness of the adaptive retrieval given by a processing framework based on Neural Networks. This processing framework makes use of the learning and generalisation capabilities of the Backpropagation learning procedure for Neural Networks to build up and use application domain knowledge in the form of a sub-symbolic knowledge representation. The knowledge is acquired from examples of queries and relevant documents of the collection in use. In the tests reported in this thesis the Cranfield document collection has been used. Three different learning strategies are introduced and analysed. Their results in terms of learning and generalisation of the application domain knowledge are studied from an Information Retrieval point of view. Their retrieval results are studied and compared with those obtained by a traditional retrieval approach. The thesis concludes with a critical analysis of the results obtained in the experimental investigation and with a critical view of the operational effectiveness of such an approach

A study of the kinematics of probabilities in information retrieval

In Information Retrieval (IR), probabilistic modelling is related to the use of a model that ranks documents in decreasing order of their estimated probability of relevance to a user's information need expressed by a query. In an IR system based on a probabilistic model, the user is guided to examine first the documents that are the most likely to be relevant to his need. If the system performed well, these documents should be at the top of the retrieved list. In mathematical terms the problem consists of estimating the probability P(R | q,d), that is the probability of relevance given a query q and a document d. This estimate should be performed for every document in the collection, and documents should then be ranked according to this measure. For this evaluation the system should make use of all the information available in the indexing term space. This thesis contains a study of the kinematics of probabilities in probabilistic IR. The aim is to get a better insight of the behaviour of the probabilistic models of IR currently in use and to propose new and more effective models by exploiting different kinematics of probabilities. The study is performed both from a theoretical and an experimental point of view. Theoretically, the thesis explores the use of the probability of a conditional, namely P(d → q), to estimate the conditional probability P(R | q,d). This is achieved by interpreting the term space in the context of the "possible worlds semantics". Previous approaches in this direction had as their basic assumption the consideration that "a document is a possible world". In this thesis a different approach is adopted, based on the assumption that "a term is a possible world". This approach enables the exploitation of term-term semantic relationships in the term space, estimated using an information theoretic measure. This form of information is rarely used in IR at retrieval time. Two new models of IR are proposed, based on two different way of estimating P(d → q) using a logical technique called Imaging. The first model is called Retrieval by Logical Imaging; the second is called Retrieval by General Logical Imaging, being a generalisation of the first model. The probability kinematics of these two models is compared with that of two other proposed models: the Retrieval by Joint Probability model and the Retrieval by Conditional Probability model. These last two models mimic the probability kinematics of the Vector Space model and of the Probabilistic Retrieval model. Experimentally, the retrieval effectiveness of the above four models is analysed and compared using five test collections of different sizes and characteristics. The results of this experimentation depend heavily on the choice of term weight and term similarity measures adopted. The most important conclusion of this thesis is that theoretically a probability transfer that takes into account the semantic similarity between the probability-donor and the probability-recipient is more effective than a probability transfer that does not take that into account. In the context of IR this is equivalent to saying that models that exploit the semantic similarity between terms in the term space at retrieval time are more effective that models that do not do that. Unfortunately, while the experimental investigation carried out using small test collections provide evidence supporting this conclusion, experiments performed using larger test collections do not provide as much supporting evidence (although they do not provide contrasting evidence either). The peculiar characteristics of the term space of different collections play an important role in shaping the effects that different probability kinematics have on the effectiveness of the retrieval process. The above result suggests the necessity and the usefulness of further investigations into more complex and optimised models of probabilistic IR, where probability kinematics follows non-classical approaches. The models proposed in this thesis are just two such approaches; other ones can be developed using recent results achieved in other fields, such as non-classical logics and belief revision theory

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.Comment: To be appear in ECIR 202

Category-Aware Location Embedding for Point-of-Interest Recommendation

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.Comment: 4 pages, 1 figure

The Impact of Psycholinguistic Patterns in Discriminating between Fake News Spreaders and Fact Checkers

[EN] Fake news is a threat to society. A huge amount of fake news is posted every day on social networks which is read, believed and sometimes shared by a number of users. On the other hand, with the aim to raise awareness, some users share posts that debunk fake news by using information from fact-checking websites. In this paper, we are interested in exploring the role of various psycholinguistic characteristics in differentiating between users that tend to share fake news and users that tend to debunk them. Psycholinguistic characteristics represent the different linguistic information that can be used to profile users and can be extracted or inferred from users¿ posts. We present the CheckerOrSpreader model that uses a Convolution Neural Network (CNN) to differentiate between spreaders and checkers of fake news. The experimental results showed that CheckerOrSpreader is effective in classifying a user as a potential spreader or checker. Our analysis showed that checkers tend to use more positive language and a higher number of terms that show causality compared to spreaders who tend to use a higher amount of informal language, including slang and swear words.The works of Anastasia Giachanou and Daniel Oberski were funded by the Dutch Research Council (grant VI.Vidi.195.152). The work of Paolo Rosso was in the framework of the XAI-DisInfodemics project on eXplainable AI for disinformation and conspiracy detection during infodemics (PLEC2021-007681), funded by the Spanish Ministry of Science and Innovation, as well as IBERIFIER, the Iberian Digital Media Research and Fact-Checking Hub funded by the European Digital Media Observatory (2020-EU-IA0252).Giachanou, A.; Ghanem, BHH.; Rissola, EA.; Rosso, P.; Crestani, F.; Oberski, D. (2022). The Impact of Psycholinguistic Patterns in Discriminating between Fake News Spreaders and Fact Checkers. Data & Knowledge Engineering. 138:1-15. https://doi.org/10.1016/j.datak.2021.10196011513

Text mining for online mental health state and personality assessment

Advances on psycho-linguistics have evidenced that the ways in which people use words could act as a reliable source to assess a wide array of behaviours. Language use acts as an indicator of the individuals’ current mental state, personality and even personal values. In this thesis, we focus on language analysis to study two closely related processes which encompass integral components of persons’ psychological profiles: mental health state and personality. Mental health state assessment by analysing online user-generated content is a field that has recently attracted considerable attention. We start this dissertation by analysing the online digital traces left by individuals in order to ascertain their mental state condition at a particular point in time. To this aim, we exploit the latent semantic structure of social media users posts to spot early traces of depression. Next, present a weak-supervision framework to derive large quantities of data for the study of depression on online settings. Moreover, we conduct a series of analytical studies aimed at gaining insights and extending the current knowledge on how mental disorders are manifested through language and online behaviour in order to be able to detect the early onset of such disorders. While the mental state condition of individuals may fluctuate over their lives, there is a core set of patterns concerning thought, affect and behaviour which is consistent across time and context, constituting the basis of what is commonly referred to as personality. In the second part of this dissertation, we focus on the computational assessment of personality from language cues. We present a novel approach to personality recognition in conversations based on capsule neural networks and exploit its inherent interpretability potential to gain insights from its inner functioning. Moreover, we propose a novel open-vocabulary approach based on multiword expressions which aims at discovering distinctive linguistic patterns of a personality trait. Such technologies will open new avenues to building more empathetic and naturalistic conversational systems