The coronavirus pandemic is having a clear impact on the supply chains of virtually all manufacturers, retailers, and wholesalers. As the world attempts to navigate through this difficult time, most ...companies are struggling to maintain a steady flow of required goods and services. Whether it is frozen foods and grocery items (i.e., toilet papers), or ventilators and masks, or even the services (i.e., clinic visits), the supply chain has been facing multiple obstacles. Most models and frameworks built in the extant literature are not been able to capture these disruptions and as such, firms are not having proper strategies to deal with. For firms with complex supply chains (i.e., manufacturing, retailing), it is indeed critical to identify strategies to deal with such a crisis. In this paper, we intend to offer strategic insights in terms of major issues firms are facing and strategic options firms are contemplating. We rely on the twitter data from NASDAQ 100 firms to generate themes regarding the issues faced by the firms and the strategies they are adopting using text analytics tools. We find that firms are facing challenges in terms of demand-supply mismatch, technology, and development of a resilient supply chain. Moreover, moving beyond profitability, firms are experiencing difficulties to construct a sustainable supply chain. We provide futuristic strategic recommendations for the rebuilding of the supply chain.
This study will discuss customers’ satisfaction with the services of Traveloka by analyzing how many people are satisfied and unhappy with the services that Traveloka has to offer. This study uses ...Twitter to acquire all the data we need, focusing only on tweets about Traveloka. The dataset is gathered from Twitter API, which consists of 1200 tweets related to Traveloka. Scikit-learn library is used through python to do the analysis process. This research employs three classification metheods: Support Vector Model (SVM), Logistic Regression, and Na¨ıve Bayes. The steps in this research were data retrieval, transformation, classification training and predicting the test data, and finally, the result analysis. Therefore, this research is looking forward to how most Twitter users feel about the performance of this mobile traveling application. The result shows that SVM has better accuracy in determining the sentiment of tweets about Traveloka.
How marginalized groups use Twitter to advance counter-narratives, preempt political spin, and build diverse networks of dissent.
The power of hashtag activism became clear in 2011, when ...#IranElection served as an organizing tool for Iranians protesting a disputed election and offered a global audience a front-row seat to a nascent revolution. Since then, activists have used a variety of hashtags, including #JusticeForTrayvon, #BlackLivesMatter, #YesAllWomen, and #MeToo to advocate, mobilize, and communicate. In this book, Sarah Jackson, Moya Bailey, and Brooke Foucault Welles explore how and why Twitter has become an important platform for historically disenfranchised populations, including Black Americans, women, and transgender people. They show how marginalized groups, long excluded from elite media spaces, have used Twitter hashtags to advance counternarratives, preempt political spin, and build diverse networks of dissent.
The authors describe how such hashtags as #MeToo, #SurvivorPrivilege, and #WhyIStayed have challenged the conventional understanding of gendered violence; examine the voices and narratives of Black feminism enabled by #FastTailedGirls, #YouOKSis, and #SayHerName; and explore the creation and use of #GirlsLikeUs, a network of transgender women. They investigate the digital signatures of the “new civil rights movement”—the online activism, storytelling, and strategy-building that set the stage for #BlackLivesMatter—and recount the spread of racial justice hashtags after the killing of Michael Brown in Ferguson, Missouri, and other high-profile incidents of killings by police. Finally, they consider hashtag created by allies, including #AllMenCan and #CrimingWhileWhite.
The term “cancel culture” has significant implications for defining discourses of digital and social media activism. In this essay, I briefly interrogate the evolution of digital accountability ...praxis as performed by Black Twitter, a meta-network of culturally linked communities online. I trace the practice of the social media callout from its roots in Black vernacular tradition to its misappropriation in the digital age by social elites, arguing that the application of useful anger by minoritized people and groups has been effectively harnessed in social media spaces as a strategy for networked framing of extant social problems. This strategy is challenged, however, by the dominant culture’s ability to narrativize the process of being “canceled” as a moral panic with the potential to upset the concept of a limited public sphere.
For a location inference model to be successful, the properties of the geotagged tweets where the location inference model is developed and those of the non-geotagged tweets where the location ...inference model is applied need to match. We investigated location mentions within the tweet text field of 3,953,166 geotagged and 2,783,609 non-geotagged tweets across five of the most prominent Twitter sources. Specifically, we compared the frequency and the location entity types used to infer the locations within the two datasets. Overall we found statistically significant differences in location mentions between the two datasets. However, although statistically significant, thirteen of the fifteen analysed location entities, showed low effect sizes. We conclude that location inference models trained on geotagged datasets can generalize a non-geotagged dataset if special adjustments are made on the development of the location inference models.
•Locations are mentioned differently in a geotagged and non-geotagged tweets.•Geotagged tweets contain a higher percentage of more precise entities.•Non-geotagged tweets contain a higher percentage of less precise entities.•Performance of location inference model is transferable to a non-geotagged dataset if conditions are met.
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