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Social Media Affordances for Connective Action
This research questions how social media use affords new forms of organizing and collective engagement. The concept of connective action has been introduced to characterize such new forms of collective engagement in which actors coproduce and circulate content based upon an issue of mutual interest. Yet, how the use of social media actually affords connective action still needed to be investigated.
Mixed methods analyses of microblogging use during the Gulf of Mexico oil spill bring insights onto this question and reveal in particular how multiple actors enacted emerging and interdependent roles with their distinct patterns of feature use. The findings allow us to elaborate upon the concept of connective affordances as collective level affordances actualized by actors in team interdependent roles. Connective affordances extend research on affordances as a relational concept by considering not only the relationships between technology and users but also the interdependence type among users and the effects of this interdependence onto what users can do with the technology. This study contributes to research on social media use by paying close attention to how distinct patterns of feature use enact emerging roles. Adding to IS scholarship on the collective use of technology, it considers how the patterns of feature use for emerging groups of actors are intricately and mutually related to each other.
Journal Article
Twitterھ : how Jack Dorsey changed the way we communicate
Discover the story of Jack Dorsey, Twitter's co-founder, and how he helped to create one of the Internet's biggest successes. Learn how Jack and his friends came up with the ideas for the business that would change their lives and the lives of so many Internet users forever.
Book
Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies
This research provides an empirical test of the “Twitter effect,” which postulates that microblogging word of mouth (MWOM) shared through Twitter and similar services affects early product adoption behaviors by immediately disseminating consumers’ post-purchase quality evaluations. This is a potentially crucial factor for the success of experiential media products and other products whose distribution strategy relies on a hyped release. Studying the four million MWOM messages sent via Twitter concerning 105 movies on their respective opening weekends, the authors find support for the Twitter effect and report evidence of a negativity bias. In a follow-up incident study of 600 Twitter users who decided not to see a movie based on negative MWOM, the authors shed additional light on the Twitter effect by investigating how consumers use MWOM information in their decision-making processes and describing MWOM’s defining characteristics. They use these insights to position MWOM in the word-of-mouth landscape, to identify future word-of-mouth research opportunities based on this conceptual positioning, and to develop managerial implications.
Journal Article
Temporal enhanced sentence-level attention model for hashtag recommendation
Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.
Journal Article
Research on CO-word network topic mining and topic differences based on haze microblog data
Some studies have shown that haze not only poses a threat to people’s health, but also affects the secretion of human hormones, making people depressed and endangering mental health. Microblog has the advantages of short content, rapid communication and convenient interaction. When the haze comes, a large number of topic microblogs related to the haze will be generated. Mining the topics of concern and psychological reactions contained in these microblogs is helpful for resource allocation and public opinion publicity in the case of haze. At present, the research of microblog topic mining in haze situation only involves a single research area, and few studies discuss the spatial differences of different regions. Based on this, this study collected the microblog data of seven provincial capitals in the severe haze areas in 2017, and used the community-based co word network method to complete a series of experimental steps, such as keyword extraction, co-occurrence matrix construction, co-word network construction and topic community detection. On this basis, we detected the topic community in the microblog data set, and analyzed the horizontal differences of topics in different cities. The results show that different cities have not only the same but also different concerns about haze. The results can provide theoretical guidance for the healthy development of cities.
Journal Article
Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms
How to improve the predictive accuracy of box office revenue with social media data is a big challenge and is particularly important for movie distributors and cinema operators. In this research, we find that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC) based on our examination of 60 movies released in China in 2012. To increase the attendance rate of movies, cinema operators can consider previous valence and volume of MUGC before scheduling the current film screenings because these messages can quickly predict the future box office revenue of a movie. Besides, we find that the volume of enterprise microblogs (i.e., MGC) can predict both box office revenue and MUGC, indicating that movie distributors should optimize their online media strategy by shifting more resources to utilizing enterprise microblogging. Although rebroadcasting volume from microblogging platforms does not predict box office revenue directly, it can indirectly predict it via MGC. Accordingly, compared with third-party platforms, rebroadcasting as one of the key distinct functions of microblogging platforms also shows its usefulness in box office revenue prediction. Overall, metrics from microblogging platforms are more effective in predicting box office revenue than those from third-party platforms.
In this research, we build a prediction model of movie box office revenue by empirically exploring its intricate relationships with user-generated content (UGC) as well as marketer-generated content (MGC) on a microblogging platform and UGC on a third-party platform. Our analyses are based on a panel vector autoregression (PVAR) model that is calibrated with a combination of data from Weibo (microblogging platform) and Douban! Movies (third party). Our empirical results show that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC). In addition, we find that the volume of enterprise microblogs (i.e., MGC) predicts box office revenue directly and also indirectly via MUGC, and MUGC thus exerts a partial mediating effect on the predictive relationship between the volume of enterprise microblogs and box office revenue. Finally, a prediction model of box office revenue using lagged box office revenue, MGC, MUGC, and DUGC is proposed, and its forecasting accuracy is found to outperform that of existing models. Managerial implications on utilizing social media for enterprises are provided.
The e-companion is available at
https://doi.org/10.1287/isre.2018.0797
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Journal Article