• Corpus ID: 221761450

Understanding Effects of Editing Tweets for News Sharing by Media Accounts through a Causal Inference Framework

  title={Understanding Effects of Editing Tweets for News Sharing by Media Accounts through a Causal Inference Framework},
  author={Kunwoo Park and Haewoon Kwak and Jisun An and Sanjay Chawla},
To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, research community does not own a sufficient level of understanding of what kinds of editing strategies are effective in promoting audience engagement. In this study, we aim to fill the gap by analyzing the current practices of media outlets using a… 

Figures and Tables from this paper

Engaging Politically Diverse Audiences on Social Media

A tool is built that integrates the model and helps journalists craft tweets that are engaging to a politically diverse audience, guided by the model predictions, and is found to be effective in seven out of the ten experiments.

A Survey on Predicting the Factuality and the Bias of News Media

This survey reviews the state of the art on media profiling for factuality and bias, arguing for the need to model them jointly and discusses interesting recent advances in using different information sources and modalities, which go beyond the text of the articles the target news outlet has published.



Spreading the news: how can journalists gain more engagement for their tweets?

Using a corpus of 1M tweets from 200 journalist Twitter accounts and audience responses to these tweets, predictive models are developed to identify the features of both journalists and news tweets that impact audience attention and proposed guidelines for journalists to maximize engagement with the news they tweet.

View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

It is shown that one can predict if an article will be publicly shared to another platform by individuals with precision of approximately 80% and has implications for news organizations desiring to increase and to prioritize types of user engagement.

Scrutinizing News Media Cooperation in Facebook and Twitter

A predictive model to increase news media popularity among readers is proposed and the results manifested that, a news media should disperse its own content and need to publish at first before other news media publish the same content in social media in-order to be popular and attract the attention from readers.

A Macroscopic Analysis of News Content in Twitter

It is found that news organizations’ accounts, across all major organizations, largely use Twitter as a professionalized, one-way communication medium to promote their own reporting, and the proportion of news media-related tweets varies vastly across different subtopics.

The Challenges of Creating Engaging Content: Results from a Focus Group Study of a Popular News Media Organization

Findings from a group study that aimed to understand the process and challenges of creating engaging content across three social media platforms in a major news organization indicate thatCreating engaging content is effort- and time-consuming, and they highlight the need to support the process of creating engage content across multiple social media Platforms.

Breaking the News: First Impressions Matter on Online News

It is discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.

Using a model of social dynamics to predict popularity of news

It is shown that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content, and that incorporating aspects of the web site design improves on predictions based on simply extrapolating from the early votes.

Characterizing the life cycle of online news stories using social media reactions

It is shown that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions, and significant improvements on the accuracy of the early prediction of shelf-life for news stories are described.

Predicting the Popularity of News Articles

This paper casts the problem of popularity prediction problem as regression, engineer several classes of features (metadata, contextual or content-based, temporal, and social), and build models for forecasting popularity, and demonstrates that it is able to accurately predict article popularity with an R ≈ 0.8 using features harvested within 30 minutes of publication time.

News from the Other Side: How Topic Relevance Limits the Prevalence of Partisan Selective Exposure

Prior research has demonstrated a preference among partisans for like-minded news outlets, a key mechanism through which the media may be polarizing Americans. But in order for source reputations to