The Effect of Knowledge Sharing on Open Source Contribution: A Multi-platform Perspective

Open source software (OSS) community plays a key role in contemporary software development. However, there is a need to better understand the factors which influence individuals’ voluntary contribution on open source platforms. In this paper, we investigate how different types of knowledge sharing affect an individuals’ contribution towards open source projects. We further refine knowledge sharing taxonomy by classifying explicit knowledge sharing into two sub-types – strong explicit knowledge sharing and weak explicit knowledge sharing, depending on the extent of interpersonal interaction required for knowledge transfer. In this paper, we take a multi-platform perspective – we collect data from GitHub – the biggest online platform to host open source software development, and Gitter – an open source instant messaging and chat room application designed for developers. We map the user identities across these two platforms. We analyze monthly panel data for the year 2017 consisting of 3,695 individuals. The results demonstrate that both strong and weak explicit knowledge sharing have positive relationship with open source contribution. Moreover, the tacit knowledge sharing positively moderates these relationships. Our paper extends the theoretical understanding of different knowledge sharing types and their inter-relationship, and their respective impact on contribution. Our findings have important implications for the OSS community, and especially help OSS platform designers get a better understanding of the symbiosis between different OSS platforms

Emotional Arousal and News Readership in Social Media

Expressions of emotions are common in news posts on social media. News providers embed emotional expressions to grab users’ attention and entice them to read the full article. However, there is a lack of empirical evidence to support this practice. We develop a theoretical model using emotions as social information theory to explain how, when and why the arousal of emotions expressed in headlines influences news article reading in social media. Through three experiments, we provide converging evidence that the use of expressed arousal backfires and reduces news reading. We also reveal a context-dependent boundary condition (i.e., information gap) and explore underlying mechanisms. Our findings speak to the growing literature on emotional expressions in social media and challenge the assumption that expressed arousal is beneficial in increasing news readership in social media

Attention-Grabbing Tactics on Social Media

News posts are popular among social media users. Since news reading is critical for both social media platforms and news providers, the use of attention-grabbing tactics, such as hyperbole and expressed emotional arousal, is commonplace to increase news readership. However, there is scant empirical evidence to explain the impact of attention-grabbing tactics on social media users’ reading intentions and behavior in social media. Therefore, I first explore how and why the use of hyperbolic statements in news headlines influences users’ interest and intention to read the news. Drawing on expectation violation theory, I propose competing hypotheses. I conducted three experiments to examine the impact of hyperbole and test the competing mechanisms. The findings from this research challenge the prevailing notion that the use of attention-grabbing tactics, such as hyperbole, is effective in piquing reader interest and influencing news readership. This research contributes to the online news consumption literature by revealing the unintended consequences of deploying hyperbole in headlines and highlighting the nuanced role of expectation violation in this emerging phenomenon. Next, I explore the role of expressed emotional arousal in news reading on social media. Expressions of emotions are common in news posts on social media. News providers embed emotional expressions to grab users’ attention and entice them to read the full article. However, there is a lack of empirical evidence to support this practice. I develop a theoretical model using emotions as social information theory to explain how, when, and why the arousal of emotions expressed in headlines influences news article reading in social media. Through three experiments, I provide converging evidence that the use of expressed arousal backfires and reduces news reading. I also reveal a context-dependent boundary condition (i.e., information gap) and explore underlying mechanisms. The findings of this research speak to the growing literature on emotional expressions in social media and challenge the assumption that expressed arousal is beneficial in increasing news readership in social media. In summary, this dissertation provides converging evidence that attention-grabbing tactics such as hyperbole and expressed emotional arousal have an unintended impact on news reading on social media. Through this dissertation, I contribute to the growing literature on news consumption on social media and challenge the commonly held assumption that attention-grabbing tactics are useful in increasing news readership on social media

Attention-Grabbing Tactics on Social Media

News posts are popular among social media users. Since news reading is critical for both social media platforms and news providers, the use of attention-grabbing tactics, such as hyperbole and expressed emotional arousal, is commonplace to increase news readership. However, there is scant empirical evidence to explain the impact of attention-grabbing tactics on social media users’ reading intentions and behavior in social media. Therefore, I first explore how and why the use of hyperbolic statements in news headlines influences users’ interest and intention to read the news. Drawing on expectation violation theory, I propose competing hypotheses. I conducted three experiments to examine the impact of hyperbole and test the competing mechanisms. The findings from this research challenge the prevailing notion that the use of attention-grabbing tactics, such as hyperbole, is effective in piquing reader interest and influencing news readership. This research contributes to the online news consumption literature by revealing the unintended consequences of deploying hyperbole in headlines and highlighting the nuanced role of expectation violation in this emerging phenomenon. Next, I explore the role of expressed emotional arousal in news reading on social media. Expressions of emotions are common in news posts on social media. News providers embed emotional expressions to grab users’ attention and entice them to read the full article. However, there is a lack of empirical evidence to support this practice. I develop a theoretical model using emotions as social information theory to explain how, when, and why the arousal of emotions expressed in headlines influences news article reading in social media. Through three experiments, I provide converging evidence that the use of expressed arousal backfires and reduces news reading. I also reveal a context-dependent boundary condition (i.e., information gap) and explore underlying mechanisms. The findings of this research speak to the growing literature on emotional expressions in social media and challenge the assumption that expressed arousal is beneficial in increasing news readership in social media. In summary, this dissertation provides converging evidence that attention-grabbing tactics such as hyperbole and expressed emotional arousal have an unintended impact on news reading on social media. Through this dissertation, I contribute to the growing literature on news consumption on social media and challenge the commonly held assumption that attention-grabbing tactics are useful in increasing news readership on social media

What We Found Will Blow Your Mind: The Impact of Hyperbole on Reader Interest and News Reading Intentions

News posts are popular among social media users. Since the reading of news is critical for both social media platforms and news providers, it is common practice for news providers to use attention-grabbing tactics, such as hyperbole, in an effort to pique user’s interest. However, there is scant empirical evidence to support that these tactics are effective. Our paper explores how and why the use of hyperbolic statements in news headlines influences users’ interest and intention to read the news. Drawing on humor and psychological contract violation literatures, we developed a theoretical model and proposed competing hypotheses. We conducted two experiments to examine the impact of hyperbole and test the competing mechanisms. Our findings challenge the prevailing notion that the use of attention-grabbing tactics, such as hyperbole, in news headlines are effective

Detecting Clickbait Using User Emotions and Behaviors on Social Media

Clickbait are tabloid news articles which lure online users to click on them, in turn increasing the click-through-rate of the landing page. We build an Emotional Classifier (EC) to detect clickbait articles by leveraging the users™ emotions and behavior