Document Based Clustering For Detecting Events in Microblogging Websites

Social media has a great in?uence in our daily lives. People share their opinions, stories, news, and broadcast events using social media. This results in great amounts of information in social media. It is cumbersome to identify and organize the interesting events with this massive volumes of data, typically browsing, searching, monitoring events becomes more and more challenging. A lot of work has been done in the area of topic detection and tracking (TDT). Most of these methods are based on single-modality (e.g., text, images) information or multi-modality information. In the single-modality analysis, many existing methods adopt visual information (e.g., images and videos) or textual information (e.g., names, time references, locations, title, tags, and description) in isolation to model event data for event detection and tracking. This problem can be resolved by a novel multi-model social event tracking and an evolutionary framework not only effectively capturing the events, but also generates the summary of these events over time. We proposed a novel method works with mmETM, which can effectively model the social documents, which includes the long text along with the images. It learns the similarities between the textual and visual modalities to separate the visual and non-visual representative topics. To incorporate our method to social tracking, we adopted an incremental learning technique represented as mmETM, which gives informative textual and visual topics of event in social media with respect to the time. To validate our work, we used a sample data set and conducted various experiments on it. Both subjective and quantitative assessments show that the proposed mmETM technique performs positively against a few best state-of-the art techniques

Evaluating Research Dataset Recommendations in a Living Lab

The search for research datasets is as important as laborious. Due to the importance of the choice of research data in further research, this decision must be made carefully. Additionally, because of the growing amounts of data in almost all areas, research data is already a central artifact in empirical sciences. Consequentially, research dataset recommendations can beneficially supplement scientific publication searches. We formulated the recommendation task as a retrieval problem by focussing on broad similarities between research datasets and scientific publications. In a multistage approach, initial recommendations were retrieved by the BM25 ranking function and dynamic queries. Subsequently, the initial ranking was re-ranked utilizing click feedback and document embeddings. The proposed system was evaluated live on real user interaction data using the STELLA infrastructure in the LiLAS Lab at CLEF 2021. Our experimental system could efficiently be fine-tuned before the live evaluation by pre-testing the system with a pseudo test collection based on prior user interaction data from the live system. The results indicate that the experimental system outperforms the other participating systems.Comment: Best of 2021 Labs: LiLA

Text Assisted Insight Ranking Using Context-Aware Memory Network

Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence. However, ranking the importance of insights remains a challenging and unexplored task. The main challenge is that explicitly scoring an insight or giving it a rank requires a thorough understanding of the tables and costs a lot of manual efforts, which leads to the lack of available training data for the insight ranking problem. In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process. We also build a dataset with text assistance. Experimental results show that our approach largely improves the ranking precision as reported in multi evaluation metrics.Comment: Accepted to AAAI 201

Urdu AI: writeprints for Urdu authorship identification

This is an accepted manuscript of an article published by ACM in ACM Transactions on Asian and Low-Resource Language Information Processing on 31/10/2021, available online at: https://doi.org/10.1145/3476467 The accepted version of the publication may differ from the final published version.The authorship identification task aims at identifying the original author of an anonymous text sample from a set of candidate authors. It has several application domains such as digital text forensics and information retrieval. These application domains are not limited to a specific language. However, most of the authorship identification studies are focused on English and limited attention has been paid to Urdu. On the other hand, existing Urdu authorship identification solutions drop accuracy as the number of training samples per candidate author reduces, and when the number of candidate author increases. Consequently, these solutions are inapplicable to real-world cases. To overcome these limitations, we formulate a stylometric feature space. Based on this feature space we use an authorship identification solution that transforms each text sample into point set, retrieves candidate text samples, and relies the nearest neighbour classifier to predict the original author of the anonymous text sample. To evaluate our method, we create a significantly larger corpus than existing studies and conduct several experimental studies which show that our solution can overcome the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous authorship identification works

Querying RDBMS using Natural Language

It is often challenging to specify queries against a relational database since SQL requires its users to know the exact schema of the database, the roles of various entities in a query, and the precise join paths to be followed. On the other hand, keyword search is unable to express many desired query semantics. In the real world, people ask questions in natural language, such as English. Theoretically, natural language interfaces for databases (NLIDBs) have many advantages over other widely accepted query interfaces (keyword-based search, form-based interface, and visual query builder). For example, a typical NLIDB would enable naive users to specify complex, ad-hoc query intent without training. Not surprisingly, an NLIDB is regarded by many as the ultimate goal. Despite these advantages, in real world applications, NLIDBs have not been widely adopted. In this dissertation, we investigate the construction of NLIDBs, specifically from the following three aspects: A natural language query is inherently ambiguous and some ambiguities may be too hard for computers to resolve. Can a system collaborate with users to achieve satisfactory reliability without burdening the user too much? The interpretation process can be considered as a mapping from a natural language query to the correct point in the semantic coverage of the NLIDB. Can the mapping process get easier by carefully defining the semantic coverage? Can an NLIDB work when no training examples are available, collect the user behavior data as the training examples and improve itself from real usage? In this dissertation, we provide affirmative answers to the above questions in the form of new query mechanism designed, techniques provided and systems constructed.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138709/1/lifei_1.pd

Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.Comment: PhD thesis, Mar 201

An Online Analytical System for Multi-Tagged Document Collections

The New York Times Annotated Corpus and the ACM Digital Library are two prototypical examples of document collections in which each document is tagged with keywords and significant phrases. Such collections can be viewed as high-dimensional document cubes against which browsers and search systems can be applied in a manner similar to online analytical processing against data cubes. The tagging patterns in these collections are examined and a generative tagging model is developed that can mimic the tag assignments observed in those collections. When a user browses the collection by means of a Boolean query over tags, the result is a subset of documents that can be summarized by a centroid derived from their document term vectors. A partial materialization strategy is developed to provide efficient storage and access to centroids for such document subsets. A customized local term vocabulary storage approach is incorporated into the partial materialization to ensure that rich and relevant term vocabulary is available for representing centroids while maintaining a low storage footprint. By adopting this strategy, summary measures dependent on centroids (including bursty terms, or larger sets of indicative documents) can be efficiently and accurately computed for important subsets of documents. The proposed design is evaluated on the two collections along with PubMed (a held-back document collection) and several synthetic collections to validate that it outperforms alternative storage strategies. Finally, an enhanced faceted browsing system is developed to support users' exploration of large multi-tagged document collections. It provides summary measures of document result sets at each step of navigation through a set of indicative terms and diverse set of documents, as well as information scent that helps to guide users' exploration. These summaries are derived from pre-materialized views that allow for quick calculation of centroids for various result sets. The utility and efficiency of the system is demonstrated on the New York Times Annotated Corpus

Enhancing Privacy and Fairness in Search Systems

Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms.Nach einer Zeit schneller Fortschritte in den Fähigkeiten digitaler Systeme beginnt die Gesellschaft zu erkennen, dass Systeme, die Menschen bei verschiedenen Aufgaben unterstützen sollen, den Einzelnen und die Gesellschaft auch schädigen können. Suchsysteme haben ein erhebliches Potenzial, um zu solchen unerwünschten Ergebnissen beizutragen, weil sie den Zugang zu Informationen vermitteln und explizit oder implizit Menschen in immer mehr Anwendungen in Ranglisten anordnen. Da sie riesige Datenmengen sowohl über Suchende als auch über Gesuchte sammeln, können sie die Privatsphäre dieser beiden Benutzergruppen verletzen. In Anwendungen, in denen Ranglisten einen Einfluss auf den finanziellen Lebensunterhalt der Menschen außerhalb der Plattform haben, z. B. auf Sharing-Economy-Plattformen oder Jobbörsen, haben Suchmaschinen eine immense wirtschaftliche Macht über ihre Nutzer, indem sie die Sichtbarkeit von Personen in Suchergebnissen kontrollieren. In dieser Dissertation werden neue Modelle und Methoden entwickelt, die verschiedene Aspekte der Privatsphäre und der Fairness in Suchsystemen, sowohl für Suchende als auch für Gesuchte, abdecken. Insbesondere leistet die Arbeit folgende Beiträge: (1) Wir schlagen ein Modell für die Berechnung von fairen Rankings vor, bei denen Suchsubjekte entsprechend ihrer Relevanz angezeigt werden. Die Sichtbarkeit wird im Laufe der Zeit durch ein Optimierungsmodell adjustiert, um die Verzerrungen der Sichtbarkeit für Sucher zu kompensieren, während die Nützlichkeit des Rankings beibehalten bleibt. (2) Wir schlagen ein Modell für die Bestimmung kritischer Suchanfragen vor, in dem für jeden Nutzer Aanfragen, die zu seinem Nutzerprofil in den Top-k-Suchergebnissen führen, herausgefunden werden. Das Problem der Berechnung von exponierenden Suchanfragen wird als Reverse-Nearest-Neighbor-Suche modelliert. Solche kritischen Suchanfragen werden dann von einem Learning-to-Rank-Modell geordnet, um die sensitiven Suchanfragen herauszufinden. (3) Wir schlagen ein Modell zur Quantifizierung von Risiken für die Privatsphäre aus Textdaten in Online Communities vor. Die Methode baut auf einem Themenmodell auf, bei dem jedes Thema durch einen Crowdsourcing-Sensitivitätswert annotiert wird. Die Risiko-Scores sind mit der Relevanz eines Benutzers mit kritischen Themen verbunden. Wir schlagen Relevanzmaße vor, die unterschiedliche Dimensionen des Benutzerinteresses an einem Thema erfassen, und wir zeigen, wie diese Maße mit der Risikowahrnehmung von Menschen korrelieren. (4) Wir schlagen ein Modell für personalisierte Suche vor, in dem die Privatsphäre geschützt wird. In dem Modell werden Suchanfragen von Nutzer partitioniert und in synthetische Profile eingefügt. Das Modell erreicht einen guten Kompromiss zwischen der Suchsystemnützlichkeit und der Privatsphäre, indem semantisch kohärente Fragmente der Suchhistorie innerhalb einzelner Profile beibehalten werden, wobei gleichzeitig angestrebt wird, die Ähnlichkeit der synthetischen Profile mit den ursprünglichen Nutzerprofilen zu minimieren. Die Modelle werden mithilfe von Informationssuchtechniken und Nutzerstudien ausgewertet. Wir benutzen eine Vielzahl von Datensätzen, die von Abfrageprotokollen über soziale Medien Postings und die Fragen vom Q&A Forums bis hin zu Artikellistungen von Sharing-Economy-Plattformen reichen