Originally published in Contemporary Psychology: APA Review of Books, 1994, Vol 39(2), 225. Reviews the book, The Nature and Origins of Mass Opinion by John Zaller (1992). The author's commendable effort to specify a model of mass opinion formation offers readers an introduction to the prevailing vi

The Nature and Origins of Mass Opinion.

The Rise of the Creative Class

Projections of Power: Framing News, Public Opinion, and U.S. Foreign Policy

A Virtuous Circle: Political Communications in Postindustrial Societies. Pippa Norris. New York: Cambridge University Press, 2000. 398 pp. $59.95 hbk. $21.95 pbk. The main finding of Pippa Norris's study is that exposure to the news media is positively related to citizens' levels of political knowledge, trust, and participation, even after controlling for demographics, attitudinal factors, and cultural differences. Although various positive contributions of exposure to news media already have been documented in other research, never was the amount of evidence as monumental as in this book. Norris analyzed European Community, Eurobarometer, European Election and Post-election, British Campaign Panel, U.S. National Election, and Pew Center for the People and the Press surveys, providing individual and aggregate level, over-time and cross-sectional evidence supporting her virtuous media thesis. The book is set up as a challenge to "conventional wisdom" or "unquestioned orthodoxy" of media malaise theories that argue that "exposure to the news media discourages learning about politics, erodes trust in political leaders and government institutions, and dampens political mobilization." The problem is that there is a huge gap between the abundance of glib charges and the scarcity of empirical evidence of negative effects of news media. Consequently, Norris mostly relies on Michael Robinson's long ago retracted videomalaise claim and strays away from her main line of theorizing by reviewing studies that deal with the issues well outside the scope of her own study, such as effects of framing news stories, political advertising, and television entertainment. Findings of negative media effects in contexts other than elections and those tapping more specific types of media content and their particular features remain unchallenged by the Norris study. Norris acknowledges that "we need to compare the effects of variance in the media messages so that we can see whether people who consistently use one distinctive source (such as crime-focused local TV news) differ from those who use others (such as rightwing talk radio)" and that "our measures of media habits are often diffuse and imprecise," but she is still restricted in her research to measures of frequency of general newspaper and television news use. Nevertheless, Norris urges us to stopblaming the news media for political problems and instead to direct our attention to "the problems themselves." This call, without showing that media in many of their specific content characteristics or structures are not a part of problems that extend beyond voting and political campaigning, seems premature at best. However, Norris's study makes a significant contribution to communication research by most convincingly showing that the news media may be an important part of solutions to some of the social ills. …

A Virtuous Circle: Political Communications in Postindustrial Societies

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

/pdf/detection-and-resolution-of-rumours-in-social-media-a-survey-4wwcvcnn3i.pdf

Detection and Resolution of Rumours in Social Media: A Survey

Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to r = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used.

/pdf/estimating-geographic-subjective-well-being-from-twitter-a-1uta568p9l.pdf

Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods.

The 2014 Indian elections on Twitter

The CL-SciSumm 2016 Shared Task is the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. The task built off of the experience and training data set created in its namesake pilot task, which was conducted in 2014 by the same organizing committee. The track included three tasks involving: (1A) identifying relationships between citing documents and the referred document, (1B) classifying the discourse facets, and (2) generating the abstractive summary. The dataset comprised 30 annotated sets of citing and reference papers from the open access research papers in the CL domain. This overview paper describes the participation and the official results of the second CL-SciSumm Shared Task, organized as a part of the Joint Workshop onBibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016), held in New Jersey,USA in June, 2016. The annotated dataset used for this shared task and the scripts used for evaluation can be accessed and used by the community at: https://github.com/WING-NUS/scisumm-corpus.

/pdf/overview-of-the-cl-scisumm-2016-shared-task-xpn9dkuabr.pdf

Overview of the CL-SciSumm 2016 Shared Task

Purpose – The purpose of this study is to analyze the macro‐level discourse structure of literature reviews found in information science journal papers, and to identify different styles of literature review writing. Although there have been several studies of human abstracting, there are hardly any studies of how authors construct literature reviews.Design/methodology/approach – This study is carried out in the context of a project to develop a summarization system to generate literature reviews automatically. A coding scheme was developed to annotate the high‐level organization of literature reviews, focusing on the types of information. Two sets of annotations were used to check inter‐coder reliability.Findings – It was found that literature reviews are written in two distinctive styles, with different discourse structures. Descriptive literature reviews summarize individual papers/studies and provide more information on each study, such as research methods, results and interpretation. Integrative liter...

Analysis of the macro‐level discourse structure of literature reviews

This study introduces and evaluates the robustness of different volumetric, sentiment, and social network approaches to predict the elections in three Asian countries – Malaysia, India, and Pakista...

/pdf/predicting-elections-from-social-media-a-three-country-three-3g8xak959i.pdf

Kokil Jaidka

Papers

Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods.

The 2014 Indian elections on Twitter

Overview of the CL-SciSumm 2016 Shared Task

Analysis of the macro‐level discourse structure of literature reviews

Predicting elections from social media: a three-country, three-method comparative study