Why the U.S. Justice Department must act against Moody's Corporation

This short note discusses the importance of the decision facing the U.S. Justice Department in the near future regarding whether or not to take action against Moody’s Corp. for its actions in the lead-up to the Financial Crisis. Having already fined Standard & Poor’s a record $1.375 billion for defrauding investors, the Justice Department faces a much different proposition. This note establishes just some of the reasons why it is imperative that Moody’s is punished, even if ultimately the punishment is less noticeable than that given to Standard & Poor’s.

Women of the corporation:a sociolinguistic perspective of senior women’s leadership language in the UK

This paper reconceptualises a classic theory (Kanter 1993[1977]) on gender and leadership in order to provide fresh insights for both sociolinguistic and management thinking. Kanter claimed that there are four approved ‘role traps’ for women leaders in male-dominated organisations: Mother, Pet, Seductress and Iron Maiden, based on familiar historical archetypes of women in power. This paper reinterprets Kanter's construct of role traps in sociolinguistic terms as gendered, discursive resources that senior women utilise proactively to interact with their predominantly male colleagues. Based on a Research Council funded1 study of 14 senior leaders (seven female and seven male) each conducting at least one senior management meeting in the U.K., the paper finds that individual speakers can transform stereotyped subject positions into powerful discursive resources to accomplish the goals of leadership, albeit marked by gender.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.