Comparing dissimilarity measures for content-based image retrieval

Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure’s retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection. From our experimental results, we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval, which will lay a foundation for developing more effective image search technologies.

Gender equality and religion:a multi-faith exploration of young adults’ narratives

This paper presents findings from research on young adults in the UK from diverse religious backgrounds. Utilizing questionnaires, interviews, and video diaries it assesses how religious young adults understood and managed the tensions in popular discourse between gender equality as an enshrined value and aspirational narrative, and religion as purportedly instituting gender inequality. We show that, despite varied understandings, and the ambivalence and tension in managing ideal and practice, participants of different religious traditions and genders were committed to gender equality. Thus, they viewed gender-unequal practices within their religious cultures as an aberration from the essence of religion. In this way, they firmly rejected the dominant discourse that religion is inherently antithetical to gender equality.

Dynamic joint sentiment-topic model

Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1) Sliding window where the current sentiment-topic word distributions are dependent on the previous sentiment-topic-specific word distributions in the last S epochs; (2) skip model where history sentiment topic word distributions are considered by skipping some epochs in between; and (3) multiscale model where previous long- and shorttimescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011. © 2013 ACM 2157-6904/2013/12-ART5 $ 15.00.

Religious faith and heterosexuality:a multi-faith exploration of young adults

This paper examines the understandings and practices of 515 heterosexual religious young adults living in the UK in terms of their religious faith and sexuality. It presents qualitative and quantitative data drawn from questionnaires, interviews, and video diaries. Four themes are explored. First, participants generally understood sexuality in relation to sacred discourses. Second, regardless of gender and religious identification, the participants drew from religious (e.g. religious community) and social (i.e. friends) influences to construct their sexual values and attitudes. Third, the religious and familial spaces within which the participants inhabited were structured by heteronormative assumptions. Thus, the participants must negotiate dominant norms, particularly those pertaining to marriage and sex within it. Finally, the paper focuses on married participants, offering insights into their motivations for, and experiences of, marriage. Overall, the paper demonstrates that, like their lesbian and gay counterparts, heterosexual religious young adults also had to manage various competing and mutually-reinforcing sexual and religious norms in constructing a meaningful life.

Religious and sexual identities:a multi-faith exploration of young adults

Presenting qualitative and quantitative findings on the lived experiences of around seven hundred young adults from Christian, Muslim, Jewish, Hindu, Buddhist, Sikh and mixed-faith backgrounds, Religious and Sexual Identities provides an illuminating and nuanced analysis of young adults' perceptions and negotiations of their religious, sexual, youth and gender identities. It demonstrates how these young adults creatively construct meanings and social connections as they navigate demanding but exciting spaces in which their multiple identities intersect. Accessible quantitative analyses are combined with rich interview and video diary narratives in this theoretically-informed exploration of religious and sexual identities in contemporary society. © Andrew Kam-Tuck Yip and Sarah-Jane Page 2013. All rights reserved.

Mainstream media behavior analysis on Twitter:a case study on UK general election

With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UK's 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the pure quantitative measures based on media coverage of various political parties. We then compare the information diffusion patterns from different categories of sources. We found that while mainstream media is good at seeding prominent information cascades, its role in shaping public opinion is being challenged by journalists since tweets from them are more likely to be retweeted and they spread faster and have longer lifespan compared to tweets from mainstream media. Moreover, the political bias of the journalists is a good indicator of the actual election results. Copyright 2013 ACM.

An empirical study on uncertainty identification in social media context

Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification. © 2013 Association for Computational Linguistics.

Topic extraction from microblog posts using conversation structures

Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.