The Impact of Crowds on News Engagement: A Reddit Case Study

Today, users are reading the news through social platforms. These platforms are built to facilitate crowd engagement, but not necessarily disseminate useful news to inform the masses. Hence, the news that is highly engaged with may not be the news that best informs. While predicting news popularity has been well studied, it has not been studied in the context of crowd manipulations. In this paper, we provide some preliminary results to a longer term project on crowd and platform manipulations of news and news popularity. In particular, we choose to study known features for predicting news popularity and how those features may change on reddit.com, a social platform used commonly for news aggregation. Along with this, we explore ways in which users can alter the perception of news through changing the title of an article. We find that news on reddit is predictable using previously studied sentiment and content features and that posts with titles changed by reddit users tend to be more popular than posts with the original article title.Comment: Published at The 2nd International Workshop on News and Public Opinion at ICWSM 201

The Salience of Fakeness: Experimental Evidence on Readers’ Distinction between Mainstream Media Content and Altered News Stories

This experiment was designed to explore people’s critical, differentiating capacity between actual news and content that looks like news. Four groups of post-millennials read four versions of a news story. While the first condition included a real news story derived from a mainstream medium, the other three conditions tested three attributes of fakeness, namely an exaggerated, satirical, and popularised frame of disinformation. Although readers differentiated between satire and the actual news story, no significant differences were observed between exaggerated and simplified versions of news and the actual news story. Additional intervening variables were scrutinized, showing a connection between the salience of a story and its perceptions of fakeness

Predicting success of online petitions from the perspective of agenda setting

Existing predictive models of online petition popularity largely overlooked the literature of agenda-setting. This study adheres to Cobb and Elder’s (1972) issue expansion model and symbolism (Birkland, 2017) in the agenda-setting literature. Examining the literature, we identified features of popular petitions and will examine the effects of these features on online petition success. Commonly used models will be used to evaluate our proposed features and to compare their performance with benchmark cases. The predictive model, i.e. the product of our study, is the combination of our proposed features and the best performing model. The contributions of the study are two-fold. This study demonstrates how we can translate the textual characteristics described by the literature of agenda-setting into technical features that are comprehensible to machines. On practical implications, a better predictive model helps activists to better utilize online platforms to secure support for their proposed policy changes

Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent Topic, Sentiment and Emotion Attributes

This paper presents a large, labelled dataset on people's responses and expressions related to the COVID-19 pandemic over the Twitter platform. From 28 January 2020 to 1 Jan 2021, we retrieved over 132 million public Twitter posts (i.e., tweets) from more than 20 million unique users using four keywords: "corona", "wuhan", "nCov" and "covid". Leveraging natural language processing techniques and pre-trained machine learning-based emotion analytic algorithms, we labelled each tweet with seventeen latent semantic attributes, including a) ten binary attributes indicating the tweet's relevance or irrelevance to the top ten detected topics, b) five quantitative emotion intensity attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from barely noticeable to extremely high intensity), and c) two qualitative attributes indicating the sentiment category and the dominant emotion category the tweet is mainly expressing. We report the descriptive statistics around the topic, sentiment and emotion attributes, and their temporal distributions, and discuss the dataset's possible usage in communication, psychology, public health, economics, and epidemiology research.Comment: Updated with the complete 2020 data (28 Jan 2020-1 Jan 2021

Using communicative patterns to predict Twitter users' social capital, likability, and popularity gains with natural language processing

Social media constructs a computer-mediated public space where individuals' visibility and influence can be quantitatively measured by the number of likes, retweets, and followers they receive. These metrics serve as a reward system that not only reflects users' popularity and social capital but also influences the climate of public opinion and deliberative democracy by encouraging and discouraging certain types of communication. Through analyzing Twitter data collected from U.S. congressional politicians and ordinary U.S. Twitter users in seven/eight waves, this study explores how communicative patterns--dual-process styles and sentiment--predict users' social capital, likability, and popularity gains on Twitter as well as how political identity and intergroup communication moderate the relationships between these variables. It found that: (a) rational expressions increase social capital and popularity gains while emotional expressions increase likability gains; (b) positive expressions generate a curvilinear effect on social capital, likability, and popularity gains in the politician dataset; (c) compared with Democratic users, Republican users receive relatively more social capital, likability, and popularity gains from emotional and negative expressions than from rational and positive expressions; (d) rational expressions lead to relatively more likability and popularity gains than emotional expressions in a group-salient context; and (e) positive expressions in ingroup/outgroup conversations generate opposite effects in the politician and ordinary user datasets. In addition, this study develops and advances computational methods in detecting communicative patterns, political identities, and intergroup communication. By implementing Distributed Dictionary Representations, this study creates metrics to measure dual-process thinking styles and sentiment in text; by developing a two-step model with deep learning using an attention mechanism, this study creates an interpretable method to detect political partisanship and intergroup communication.Includes bibliographical references

Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to dismantling strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms

Identity as a compass for understanding media choice

The changes to our socio-technological media environment over the past 30 years have heightened the interest in identity across the social sciences. The spread of networked digital communication technologies and mobile media have increased the urgency for media scholars to better understand how and why individuals consume media as they do. Several media choice scholars have recently started considering how individuals’ identity and self-concept relate to media choice, but have not yet systematically addressed how identity might be related. This dissertation takes the first steps toward advancing an identity-based approach to understanding individual media choice in the 21st century by: 1) Providing a thorough theoretical and conceptual review of identity theory (Burke & Stets, 2009) and the identity process; 2) By discussing media research in the context of identity theory and applying identity theory directly to media research, and; 3) By empirically testing multiple elements of identity theory in two original experimental designs. Results indicate that identity not only affects media choice, it also affects how individuals ascribe meaning to media content