A Topic Recommender for Journalists

The way in which people acquire information on events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What to Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles

A Bayesian-Based Approach for Public Sentiment Modeling

Public sentiment is a direct public-centric indicator for the success of effective action planning. Despite its importance, systematic modeling of public sentiment remains untapped in previous studies. This research aims to develop a Bayesian-based approach for quantitative public sentiment modeling, which is capable of incorporating uncertainty and guiding the selection of public sentiment measures. This study comprises three steps: (1) quantifying prior sentiment information and new sentiment observations with Dirichlet distribution and multinomial distribution respectively; (2) deriving the posterior distribution of sentiment probabilities through incorporating the Dirichlet distribution and multinomial distribution via Bayesian inference; and (3) measuring public sentiment through aggregating sampled sets of sentiment probabilities with an application-based measure. A case study on Hurricane Harvey is provided to demonstrate the feasibility and applicability of the proposed approach. The developed approach also has the potential to be generalized to model various types of probability-based measures

No cause for celebration: the rise of celebrity news values in the British quality press

In their study of news values in in the British press Harcup and O’Neill (2001) noted that celebrity was one of the redefinitions of the ‘taxonomy of news values for the twenty-first century’. At the time, Harcup and O’Neill made no judgement about the changes in news values in their redefinition, nor did their research focus on the relative importance and potency of certain news values in the hierarchy of news. Using celebrity case studies from recent decades in the British ‘quality’ press, this article seeks to do just that, demonstrating that the pervasiveness and volume of coverage of celebrity has risen exponentially over 30-plus years. Celebrity/entertainment news values would appear to have risen much higher up the hierarchy of news, guaranteeing extensive coverage if combined with other news values such as surprise and bad news. The findings give rise to a wider debate and concerns about the colonisation of celebrity news and dumbing down in so many areas of British journalism, and the implications for the public and educators

Popularity Evolution of Professional Users on Facebook

Popularity in social media is an important objective for professional users (e.g. companies, celebrities, and public figures, etc). A simple yet prominent metric utilized to measure the popularity of a user is the number of fans or followers she succeed to attract to her page. Popularity is influenced by several factors which identifying them is an interesting research topic. This paper aims to understand this phenomenon in social media by exploring the popularity evolution for professional users in Facebook. To this end, we implemented a crawler and monitor the popularity evolution trend of 8k most popular professional users on Facebook over a period of 14 months. The collected dataset includes around 20 million popularity values and 43 million posts. We characterized different popularity evolution patterns by clustering the users temporal number of fans and study them from various perspectives including their categories and level of activities. Our observations show that being active and famous correlate positively with the popularity trend

Internet media planning : an optimization model

Of the various media vehicles available for advertising, the internet is the latest and the most rapidly growing, emerging as the ideal medium to promote products and services in the global market. In this article, the authors propose an internet media planning model whose main objective is to help advertisers determine the return they obtain from spending on internet advertising. Using available data such as internet page view and advertising performance data, the model contributes to attempts not only to optimize the internet advertising schedule but also to fix the right price for internet advertisements on the basis of the characteristics of the exposure distribution of sites. The authors test the model with data provided by KoreanClick, a Korean market research company that specializes in internet audience measurement. The optimal durations for the subject sites provide some useful insights. The findings contrast with current web media planning practices, and the authors demonstrate the potential savings that could be achieved if their approach were applied.media planning; optimization; advertising repeat exposure; probability distribution; internet

Single-Parent Families and Their Impact on Children: Changing Portrayals in Popular Magazines in the U.S., 1900-1998

Survey data indicate that Americans have become increasingly accepting of single-parent families formed through divorce and non-marital childbearing since 1960 (Thornton 1989; Thornton and Young-DeMarco 2001; Pagnini and Rindfuss 1993). But knowledge of attitudes about single-parent families is limited in terms of both time period and detail. Most data series do not begin until after 1950 (Thornton 1995) and focus narrowly on measuring views of the demographic trends that have fueled the increase in single-parent families rather than on a broader set of attitudes about single-parent families or factors that might influence these attitudes.