Nationality identi cation unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classi ers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support ne-grained classi cation. We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classi ers and other systems. rough our analysis of 57M contact lists from a major Internet company, we are able to design a ne-grained nationality classi er covering 39 groups representing over 90% of the world population. In an evaluation against other published systems over 13 common classes, our F1 score (0.795) is substantial be er than our closest competitor Ethnea (0.580). To the best of our knowledge, this is the most accurate, ne-grained nationality classi er available.As a social media application, we apply our classi ers to the followers of major Twi er celebrities over six di erent domains. We demonstrate stark di erences in the ethnicities of the followers of Trump and Obama, and in the sports and entertainments favored by di erent groups. Finally, we identify an anomalous political gure whose presumably in ated following appears largely incapable of reading the language he posts in.

Online reviews are an important source for consumers to evaluate products/services on the Internet (e.g. Amazon, Yelp, etc.). However, more and more fraudulent reviewers write fake reviews to mislead users. To maximize their impact and share effort, many spam attacks are organized as campaigns, by a group of spammers. In this paper, we propose a new two-step method to discover spammer groups and their targeted products. First, we introduce NFS (Network Footprint Score), a new measure that quantifies the likelihood of products being spam campaign targets. Second, we carefully devise GroupStrainer to cluster spammers on a 2-hop subgraph induced by top ranking products. We demonstrate the efficiency and effectiveness of our approach on both synthetic and real-world datasets from two different domains with millions of products and reviewers. Moreover, we discover interesting strategies that spammers employ through case studies of our detected groups.

A news article's title, content and link structure often reveal its political ideology. However, most existing works on automatic political ideology detection only leverage textual cues. Drawing inspiration from recent advances in neural inference, we propose a novel attention based multi-view model to leverage cues from all of the above views to identify the ideology evinced by a news article. Our model draws on advances in representation learning in natural language processing and network science to capture cues from both textual content and the network structure of news articles. We empirically evaluate our model against a battery of baselines and show that our model outperforms state of the art by 10 percentage points F1 score.

When a digital library user searches for publications by an author name, she often sees a mixture of publications by different authors who have the same name. With the growth of digital libraries and involvement of more authors, this author ambiguity problem is becoming critical. Author disambiguation (AD) often tries to solve this problem by leveraging metadata such as coauthors, research topics, publication venues and citation information, since more personal information such as the contact details is often restricted or missing. In this paper, we study the problem of how to efficiently disambiguate author names given an incessant stream of published papers. To this end, we propose a ''BatchAD?IncAD'' framework for dynamic author disambiguation. First, we perform batch author disambiguation (BatchAD) to disambiguate all author names at a given time by grouping all records (each record refers to a paper with one of its author names) into disjoint clusters. This establishes a one-to-one mapping between the clusters and realworld authors. Then, for newly added papers, we periodically perform incremental author disambiguation (IncAD), which determines whether each new record can be assigned to an existing cluster, or to a new cluster not yet included in the previous data. Based on the new data, IncAD also tries to correct previous AD results. Our main contributions are: (1) We demonstrate with real data that a small number of new papers often have overlapping author names with a large portion of existing papers, so it is challenging for IncAD to effectively leverage previous AD results. (2) We propose a novel IncAD model which aggregates metadata from a cluster of records to estimate the author's profile such as her coauthor distributions and keyword distributions, in order to predict how likely it is that a new record is ''produced'' by the author. (3) Using two labeled datasets and one large-scale raw dataset, we show that the proposed method is much more efficient than state-of-the-art methods while ensuring high accuracy.

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