Marta Arias

Forecasting with twitter data

The dramatic rise in the use of social network platforms such as Facebook or Twitter has resulted in the availability of vast and growing user-contributed repositories of data. Exploiting this data by extracting useful information from it has become a great challenge in data mining and knowledge discovery. A recently popular way of extracting useful information from social network platforms is to build indicators, often in the form of a time series, of general public mood by means of sentiment analysis. Such indicators have been shown to correlate with a diverse variety of phenomena. In this article we follow this line of work and set out to assess, in a rigorous manner, whether a public sentiment indicator extracted from daily Twitter messages can indeed improve the forecasting of social, economic, or commercial indicators. To this end we have collected and processed a large amount of Twitter posts from March 2011 to the present date for two very different domains: stock market and movie box office revenue. For each of these domains, we build and evaluate forecasting models for several target time series both using and ignoring the Twitter-related data. If Twitter does help, then this should be reflected in the fact that the predictions of models that use Twitter-related data are better than the models that do not use this data. By systematically varying the models that we use and their parameters, together with other tuning factors such as lag or the way in which we build our Twitter sentiment index, we obtain a large dataset that allows us to test our hypothesis under different experimental conditions. Using a novel decision-tree-based technique that we call summary tree we are able to mine this large dataset and obtain automatically those configurations that lead to an improvement in the prediction power of our forecasting models. As a general result, we have seen that nonlinear models do take advantage of Twitter data when forecasting trends in volatility indices, while linear ones fail systematically when forecasting any kind of financial time series. In the case of predicting box office revenue trend, it is support vector machines that make best use of Twitter data. In addition, we conduct statistical tests to determine the relation between our Twitter time series and the different target time series.

Compact routing with name independence

This paper is concerned with compact routing in the name independent model first introduced by Awerbuch et al. [1] for adaptive routing in dynamic networks. A compact routing scheme that uses local routing tables of size <i>Õ(n^1/2)</i>, <i>O(log² n)</i>-sized packet headers, and stretch bounded by 5 is obtained. Alternative schemes reduce the packet header size to <i>O(log n)</i> at cost of either increasing the stretch to 7, or increasing the table size to <i>Õ(n^2/3)</i>. For smaller table-size requirements, the ideas in these schemes are generalized to a scheme that uses <i>O(log² n)</i>-sized headers, <i>Õ(k²n^2/k)</i>-sized tables, and achieves a stretch of <i>min[1 + (k-1)(2^k/2-2), 16k²+4k ]</i>, improving the best previously-known name-independent scheme due to Awerbuch and Peleg [3].

Real-time ranking with concept drift using expert advice

In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of computer networks, one might want to generate lists of nodes ranked according to their susceptibility to attack. In addition, real-world data streams often exhibit concept drift, making the learning task even more challenging. We present an online learning approach to ranking with concept drift, using weighted majority techniques. By continuously modeling different snapshots of the data and tuning our measure of belief in these models over time, we capture changes in the underlying concept and adapt our predictions accordingly. We measure the performance of our algorithm on real electricity data as well as asynthetic data stream, and demonstrate that our approach to ranking from stream data outperforms previously known batch-learning methods and other online methods that do not account for concept drift.

Construction and learnability of canonical Horn formulas

We describe an alternative construction of an existing canonical representation for definite Horn theories, the Guigues-Duquenne basis (or GD basis), which minimizes a natural notion of implicational size. We extend the canonical representation to general Horn, by providing a reduction from definite to general Horn CNF. Using these tools, we provide a new, simpler validation of the classic Horn query learning algorithm of Angluin, Frazier, and Pitt, and we prove that this algorithm always outputs the GD basis regardless of the counterexamples it receives.

Compact Routing with Name Independence

This paper is concerned with compact routing schemes for arbitrary undirected networks in the name-independent model first introduced by Awerbuch, Bar-Noy, Linial, and Peleg. A compact routing scheme that uses local routing tables of size

Learning closed horn expressions

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An Approach to Software Testing of Machine Learning Applications

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Canonical horn representations and query learning

O(\log^2 n)

Polynomial certificates for propositional classes

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Learning regulatory programs that accurately predict differential expression with MEDUSA

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