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151.
Recent years have been characterized by the ubiquitous use of social networks as a mean of self and social identity, which offers new opportunities for qualitative and quantitative research in social sciences. The dynamics of interactions on social platforms such as Twitter promote the development of social movements around hashtags, such as #MeToo. According to previous research, this movement has set the beginning of an era. The present study aims to determine the key indicators of social identity in the #MeToo movement in Twitter using textual analysis and sentiment analysis of user-generated content. To this end, we use a cognitive pragmatics point of view to study a corpus of 31.305 tweets. Using the methodological approaches of corpus linguistics (CL) and discourse analysis (DA), we identify keywords, topics, frequency, and n-grams or collocations to understand the social identity of the #MeToo movement. The key indicators of the social identity in the #MeToo Era are validated using association statistical measures of Log-Likelihood and Mutual Information (MI). Our results reveal the polarization of sentiments where UGC is associated with both negative and positive topics. The social identity is particularly strongly correlated with women and the workplace. Finally, regardless the industry or area, these results present a holistic approach to the social identity of #MeToo. 相似文献
152.
This study examines Twitter use by the central government in Korea and the federal government in the USA by employing the webometric technique to extract their Twitter activity (basic Twitter statistics such as the numbers of followers, followings, and Tweets) and the social network analysis technique to map the relationship between their Twitter accounts and the direction of outlinks in their Tweets. The results of the initial analysis indicate some differences in Twitter strategies between the two governments. For example, Korean ministries were well connected through a dense network, engaged in collective cooperation, and retweeted common content to reinforce their collective agendas regardless of their main administrative functions, whereas US government departments were less collective and more individualistic and retweeted those messages that specifically fit the purpose of each department. In addition, the results for outlinks indicate that US government departments preferred private sources of information, whereas Korean ministries, government sources. 相似文献
153.
Unstructured tweet feeds are becoming the source of real-time information for various events. However, extracting actionable information in real-time from this unstructured text data is a challenging task. Hence, researchers are employing word embedding approach to classify unstructured text data. We set our study in the contexts of the 2014 Ebola and 2016 Zika outbreaks and probed the accuracy of domain-specific word vectors for identifying crisis-related actionable tweets. Our findings suggest that relatively smaller domain-specific input corpora from the Twitter corpus are better in extracting meaningful semantic relationship than generic pre-trained Word2Vec (contrived from Google News) or GloVe (of Stanford NLP group). However, domain-specific quality tweet corpora during the early stages of outbreaks are normally scant, and identifying actionable tweets during early stages is crucial to stemming the proliferation of an outbreak. To overcome this challenge, we consider scholarly abstracts, related to Ebola and Zika virus, from PubMed and probe the efficiency of cross-domain resource utilization for word vector generation. Our findings demonstrate that the relevance of PubMed abstracts for the training purpose when Twitter data (as input corpus) would be scant during the early stages of the outbreak. Thus, this approach can be implemented to handle future outbreaks in real time. We also explore the accuracy of our word vectors for various model architectures and hyper-parameter settings. We observe that Skip-gram accuracies are better than CBOW, and higher dimensions yield better accuracy. 相似文献
154.
This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion. 相似文献
155.
Twitter and other social media technologies offer social scientists new and innovative ways of investigating individual-level processes on a mass scale. The current study examined communicative behaviors associated with Cutting off Reflected Failure (CORF) in the aftermath of the 2014 Scottish Independence Referendum. The results support past research suggesting that following defeat, small numbers of highly involved individuals may not experience CORF but may instead turn to other rationalizations to make sense of the event in question. Findings are discussed in terms of our understanding of Basking in Reflected Glory (BIRG) and CORF, and in terms of the utility of Twitter in examining similar phenomena in the future. 相似文献
156.
Stacey L. Kerr Mardi J. Schmeichel 《Journal of Research on Technology in Education》2018,50(3):241-252
Gender differences in participation were examined across four Twitter chats for social studies teachers. Analyses drawing on mixed methods revealed that while there was parity across most kinds of tweets, participants identified as men were more likely to use the examined Twitter chats to share resources, give advice, boast, promote their own blog/resource/website, and offer critique to another participants' tweet. Participants identified as women were more likely to write tweets that included positive affirmations for other chat participants. These findings suggest that there are differences in the way that women and men tend to participate in teacher Twitter chat spaces. 相似文献
157.
Benjamin Gleason 《Learning, Media and Technology》2018,43(2):165-180
This research investigates how three high school students in the USA developed new literacies practices through their participation in teenage Twitter. Data was collected from two sources, including archival data from participants’ Twitter over a two-year span, and semi-structured interviews. Results found that teenagers developed a number of practices that facilitated orientation to cultural conventions of teenage Twitter, helped them mobilize followers for participatory events, and led to reflective awareness of how to tell stories on Twitter. This study suggests that teenagers used the affordances of Twitter in order to craft multimodal narratives that are co-constructed, participatory, nonlinear, and emergent. Thinking in hashtags, for participants, is a kind of action that serves to develop affinities of relation (to friends, to pop culture, and to new knowledge) through mediatized ‘vital life stuff.’ 相似文献
158.
《Information processing & management》2022,59(2):102811
Open data is becoming ubiquitous as governments, companies, and even individuals have the option to offer more or less unrestricted access to their non-sensitive data. The benefits of open data, such as accessibility and transparency, have motivated and enabled a large number of research studies and applications in both academia and industry. However, each open data only offers a single perspective, and its potential inherent limitations (e.g., demographic biases) may lead to poor decisions and misjudgments. This paper discusses how to create and use multiple digital lenses empowered by open data, including census data (macro lens), search logs (meso lens), and social data (micro lens), to investigate general real-world events. To reveal the unique angles and perspectives brought by each open lens, we summarize and compare the underpinning open data from eleven dimensions, such as utility, data volume, dynamic variability, and demographic fairness. Then, we propose an easy-to-use and generalized open data driven framework, which automatically retrieves multi-source data, extracts features, and trains machine learning models for the event specified by answering what, when, and where questions. With low labor efforts, the framework’s generalization and automation capabilities guarantee an instant investigation of general events and phenomena, such as disasters, sports events, and political activities. We also conduct two case studies, i.e., the COVID-19 pandemic and Great American Eclipse (see Appendix), to demonstrate its feasibility and effectiveness at different time granularities. 相似文献