What is the role of the internet in politics general and during campaigns in particular? And what is the role of large amounts of user data in all of this?

Social media responses to news have increasingly gained in importance as they can enhance a consumer's news reading experience, promote information sharing and aid journalists in assessing their readership's response to a story. Given that the number of responses to an online news article may be huge, a common challenge is that of selecting only the most interesting responses for display. This paper addresses this challenge by casting message selection as an optimization problem. We define an objective function which jointly models the messages' utility scores and their entropy. We propose a near-optimal solution to the underlying optimization problem, which leverages the submodularity property of the objective function. Our solution first learns the utility of individual messages in isolation and then produces a diverse selection of interesting messages by maximizing the defined objective function. The intuitions behind our work are that an interesting selection of messages contains diverse, informative, opinionated and popular messages referring to the news article, written mostly by users that have authority on the topic. Our intuitions are embodied by a rich set of content, social and user features capturing the aforementioned aspects. We evaluate our approach through both human and automatic experiments, and demonstrate it outperforms the state of the art. Additionally, we perform an in-depth analysis of the annotated ``interesting'' responses, shedding light on the subjectivity around the selection process and the perception of interestingness.

E-commerce companies are increasingly encouraging their users to connect to social media venues such as Facebook and Pinterest. The main strategic goal of such social connections is to boost user interaction and adoption on social media. However only a few efforts have been focused so far on leveraging users' social profiles to personalize the e-commerce experience and to recommend products of interest. In this paper, we start exploring this topic by investigating if a user's social media profile can be used to predict and recommend what type of products and what brands the social user is more likely to buy. More specifically, we study the correlation between the brands liked by the user on social media sites and those purchased on an e-commerce site. We then leverage these correlations in a brand prediction system, showing that social media can be effectively used to recommend branded products when user-user collaborative filtering techniques are used.

The tutorial will summarize the state-of-the art in the growing area of computational political science. Like many others, this research domain is being revolutionized by the availability of open, big data and the increasing reach and importance of social media. The surging interest on the part of the academic community is matched by intense efforts on the part of political campaigns to use online data in order to learn how to best disseminate information and reach the right potential donors or voters. In this context, a tutorial can summarize existing methods in a fascinating, high-interest area and allow participants with diverse backgrounds to get inspiration from the methods and problems studied. The tutorial will feature seminal research concerning (i) political polarization, (ii) election prediction and polling, and (iii) political campaigning and influence propagation. The goal is not only to familiarize attendees with ideas from related conferences such as WWW, ICWSM or CIKM, but also to present ideas and quantitative methods closer to political science such as Poole's and Rosenthal's NOMINATE score for a politician's political orientation.

In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.

In this paper we introduce the task of "tweet recommendation", the problem of suggesting tweets that match a user's interests and likes. We propose an Information-Retrieval-like model that leverages the content of the user's tweets and those of her friends, and that effectively retrieves a set of tweets that is personalized and varied in nature. Our approach could be easily leveraged to build, for example, a Twitter or Facebook timeline that collects messages that are of interest for the user, but that are not posted by her friends. We compare to typical approaches used in similar tasks, reporting significant gains in terms of overall precision, up to about +20%, on both a corpus-based evaluation and real world user study.

What is the role of the internet in politics general and during campaigns in particular? And what is the role of large amounts of user data in all of this? In the 2008 U.S. presidential campaign the Democrats were far more successful than the Republicans in utilizing online media for mobilization, co-ordination and fundraising. For the first time, social media and the Internet played a fundamental role in political campaigns. However, technical research in this area has been surprisingly limited and fragmented. The goal of this workshop is to bring together, for the first time, researchers working at the intersection of social network analysis, computational social science and political science, to share and discuss their ideas in a common forum; and to inspire further developments in this growing, fascinating field. The workshop has Filippo Menczer as keynote speaker, it includes technical presentations of accepted papers and concludes with a panel discussion where scientists and media experts from different fields can interact and share views.

More and more technologies are taking advantage of the explosion of social media (Web search, content recommendation services, marketing, ad targeting, etc.). This paper focuses on the problem of automatically constructing user profiles, which can significantly benefit such technologies. We describe a general and robust machine learning framework for large-scale classification of social media users according to dimensions of interest. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business.

Social media provides researchers with continuously updated information about developments of interest to large audiences. This paper addresses the task of identifying controversial events using Twitter as a starting point: we propose 3 models for this task and report encouraging initial results.

The distributional hypothesis states thatwords with similar distributional properties have similar semantic properties(Harris 1968). This perspective on word semantics, was early discussed in linguistics (Firth 1957; Harris 1968), and then successfully applied to Information Retrieval (Salton, Wong and Yang 1975). In Information Retrieval, distributional notions (e.g. document frequency and word co-occurrence counts) have proved a key factor of success, as opposed to early logic-based approaches to relevance modeling (van Rijsbergen 1986; Chiaramella and Chevallet 1992; van Rijsbergen and Lalmas 1996).

Abstract In this article, we underpin the intuition that frame semantic information is a useful resource for modelling textual entailment. To this end, we provide a manual frame semantic annotation for the test set used in the second recognizing textual entailment (RTE) challenge – the FrameNet-annotated textual entailment (FATE) corpus – and discuss experiments we conducted on this basis. In particular, our experiments show that the frame semantic lexicon provided by the Berkeley FrameNet project provides surprisingly good coverage for the task at hand. We identify issues of automatic semantic analysis components, as well as insufficient modelling of the information provided by frame semantic analysis as reasons for ambivalent results of current systems based on frame semantics.

Abstract Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so.

In this paper, we present Espresso, a weakly-supervised, general-purpose, and accurate algorithm for harvesting semantic relations. The main contributions are: i) a method for exploiting generic patterns by filtering incorrect instances using the Web; and ii) a principled measure of pattern and instance reliability enabling the filtering algorithm. We present an empirical comparison of Espresso with various state of the art systems, on different size and genre corpora, on extracting various general and specific relations. Experimental results show that our exploitation of generic patterns substantially increases system recall with small effect on overall precision.

Many algorithms have been developed to harvest lexical semantic resources, however few have linked the mined knowledge into formal knowledge repositories.In this paper, we propose two algorithms for automatically ontologizing (attaching) semantic relations into WordNet.We present an empirical evaluation on the task of attaching partof and causation relations, showing an improvement on F-score over a baseline model.

In this paper we investigate a novel method to detect asymmetric entailment relations between verbs. Our starting point is the idea that some point-wise verb selectional preferences carry relevant semantic information. Experiments using Word-Net as a gold standard show promising results. Where applicable, our method, used in combination with other approaches, significantly increases the performance of entailment detection. A combined approach including our model improves the AROC of 5% absolute points with respect to standard models.