Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter

There is a strong interest among academics and practitioners in studying branding issues in the big data era. In this article, we examine the sentiments toward a brand, via brand authenticity, to identify the reasons for positive or negative sentiments on social media. Moreover, in order to increase precision, we investigate sentiment polarity on a five-point scale. From a database containing 2,282,912 English tweets with the keyword ‘Starbucks’, we use a set of 2204 coded tweets both for analyzing brand authenticity and sentiment polarity. First, we examine the tweets qualitatively to gain insights about brand authenticity sentiments. Then we analyze the data quantitatively to establish a framework in which we predict both the brand authenticity dimensions and their sentiment polarity. Through three qualitative studies, we discuss several tweets from the dataset that can be classified under the quality commitment, heritage, uniqueness, and symbolism categories. Using latent semantic analysis (LSA), we extract the common words in each category. We verify the robustness of previous findings with an in-lab experiment. Results from the support vector machine (SVM), as the quantitative research method, illustrate the effectiveness of the proposed procedure of brand authenticity sentiment analysis. It shows high accuracy for both the brand authenticity dimensions’ predictions and their sentiment polarity. We then discuss the theoretical and managerial implications of the studies.     

ENGLISH HEGEMONISM AND CULTURE IMPERIALISM

English is the main link language across cultures today.The native English speakers benefit most from the English hegemonism for they share the English centered information flows and the recognitions of the scientific achievements.English native users may be more competitive in academic fields and other related industries(such as publishing industry)because of the language they speak.And the problem of endangered languages is essentially due to the worldwide spread and hegemony of English in the world.The worldwide use of English has destroyed the linguistic diversity of the world.     

Work from home during the COVID-19 pandemic: An observational study based on a large geo-tagged COVID-19 Twitter dataset (UsaGeoCov19)

As COVID-19 swept over the world, people discussed facts, expressed opinions, and shared sentiments about the pandemic on social media. Since policies such as travel restriction and lockdown in reaction to COVID-19 were made at different levels of the society (e.g., schools and employers) and the government, we build a large geo-tagged Twitter dataset titled UsaGeoCov19 and perform an exploratory analysis by geographic location. Specifically, we collect 650,563 unique geo-tagged tweets across the United States covering the date range from January 25 to May 10, 2020. Tweet locations enable us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspire us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights.UsaGeoCov19 link: http://yunhefeng.me/geo-tagged_twitter_datasets/.     

Understanding public opinions on social media for financial sentiment analysis using AI-based techniques

The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (～ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding.     

Challenges of conducting survey research related to a social protest movement: Lessons learned from a study of Gezi protests involving the Turkish diaspora in three European countries

Research on social protest movements raises many complicated methodological issues. This article systematically explains the methodological quandaries the authors confronted when studying demonstrations and online and offline activism by ethnic Turks in Germany, Belgium, and the Netherlands in support of the Gezi Park protesters in Turkey. It explains how participants were recruited and surveyed in this complex and extremely sensitive environment. It offers lessons learned that can be applied to other studies involving surveys of ethnic minorities engaged in social protest movements. More generally, they may also apply to surveys of any vulnerable population about sensitive issues.     

Content features of tweets for effective communication during disasters: A media synchronicity theory perspective

Users’ ability to retweet information has made Twitter one of the most prominent social media platforms for disseminating emergency information during disasters. However, few studies have examined how Twitter’s features can support the different communication patterns that occur during different phases of disaster events. Based on the literature of disaster communication and Media Synchronicity Theory, we identify distinct disaster phases and the two communication types—crisis communication and risk communication—that occur during those phases. We investigate how Twitter’s representational features, including words, URLs, hashtags, and hashtag importance, influence the average retweet time—that is, the average time it takes for retweet to occur—as well as how such effects differ depending on the type of disaster communication. Our analysis of tweets from the 2013 Colorado floods found that adding more URLs to tweets increases the average retweet time more in risk-related tweets than it does in crisis-related tweets. Further, including key disaster-related hashtags in tweets contributed to faster retweets in crisis-related tweets than in risk-related tweets. Our findings suggest that the influence of Twitter’s media capabilities on rapid tweet propagation during disasters may differ based on the communication processes.     

Do your social media lead you to make social deal purchases? Consumer-generated social referrals for sales via social commerce

The advent of social commerce has resulted in a new business model for e-commerce. Although studies on this business model have increased over time, they have paid less attention to its core business model: consumer-generated social influence on sales on a social commerce site. Therefore, in this paper, we examine the effect on sales of social sharing, such as Facebook “likes” and Twitter tweets, which generate social influence, using data from major social commerce companies. We find that consumer-generated social referrals regarding deals significantly boost sales in social commerce. When we examine deals involved in national sales, this finding holds only for Facebook but not for tweets. These findings have the implication for managers that not all social referrals are meaningful in increasing sales for their business.     

英汉语篇衔接手段的对比与分析

韩礼德和哈桑提出的衔接理论对语篇分析产生了重要影响。通过对英汉语篇的衔接手段进行对比分析,发现从总体上看,英语语篇衔接手段的使用多于汉语语篇。这一现象与英汉两种语言各自的语法结构有关,同时也与两种语言所处的社会文化背景联系紧密。本文通过分析英汉语篇衔接手段的不同,试图找到出现这些差异的深层原因,从而促进跨文化交际,提高中国的英语学习者运用英语衔接手段的水平,并为英汉汉英翻译提供启示。     

Two-stage NER for tweets with clustering

One main challenge of Named Entities Recognition (NER) for tweets is the insufficient information in a single tweet, owing to the noisy and short nature of tweets. We propose a novel system to tackle this challenge, which leverages redundancy in tweets by conducting two-stage NER for multiple similar tweets. Particularly, it first pre-labels each tweet using a sequential labeler based on the linear Conditional Random Fields (CRFs) model. Then it clusters tweets to put tweets with similar content into the same group. Finally, for each cluster it refines the labels of each tweet using an enhanced CRF model that incorporates the cluster level information, i.e., the labels of the current word and its neighboring words across all tweets in the cluster. We evaluate our method on a manually annotated dataset, and show that our method boosts the F1 of the baseline without collectively labeling from 75.4% to 82.5%.     

Web document summarization by exploiting social context with matrix co-factorization

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.     

Building parallel corpora by automatic title alignment using length-based and text-based approaches

Cross-lingual semantic interoperability has drawn significant attention in recent digital library and World Wide Web research as the information in languages other than English has grown exponentially. Cross-lingual information retrieval (CLIR) across different European languages, such as English, Spanish, and French, has been widely explored; however, CLIR across European languages and Oriental languages is still in the initial stage. To cross language boundary, corpus-based approach is promising to overcome the limitation of the knowledge-based and controlled vocabulary approaches but collecting parallel corpora between European language and Oriental language is not an easy task. Length-based and text-based approaches are two major approaches to align parallel documents. In this paper, we investigate several techniques using these approaches and compare their performances in aligning English and Chinese titles of parallel documents available on the Web.     

Exploring key indicators of social identity in the #MeToo era: Using discourse analysis in UGC

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.     

从认知语言学角度透视高校英语教学中的文化教学

随着我国社会主义市场经济的进步和发展,英语教学在教育事业中所占的地位越来越重要,尤其是在我国加入WTO以后,不论是经济建设,还是政治建设,都在朝着国际化的方向发展,与其他国家的合作越来越紧密。英语作为国际交流的主要语言之一,直接关系着彼此的合作。鉴于此,我国各教育单位必须要高度重视英语教学工作的效率和质量,培养出更多的英语人才,以此促进我国的全面发展。本文针对如何从认知语言学角度看待高校英语的文化教学做了研究。     

You talkin’ to me? Exploring Human/Bot Communication Patterns during Riot Events

We analyze a data-set including more than 4.5 million tweets related to four highly emotional riot events. In particular, we examine statistically significant structural patterns that emerge as humans directly engage in an exchange of emotional messages with other humans on Twitter. Furthermore, we compare typical human-to-human communication patterns with those that emerge as bots engage in an emotional message-exchange with human users. To this end, we apply the novel concept of emotion-exchange motifs. We found that a) human-to-human conversations results in a variety of motifs that contain reciprocal edges and self-loops, b) bots predominantly contribute to the emergence of message broadcasting, single-way message sending behavior, c) in contrast to previous findings we found that in certain events bots frequently engage in direct message exchanges with humans, d) during riot events bots tend to direct fear-conveying messages to human users.     

Undermining anarchy: Facebook's influence on anarchist principles of organization in Occupy Wall Street

Drawing on concepts rooted in cybernetics and anarchist political theory, this article argues that the shift in Occupy Wall Street from being a physical protest camp in late 2011 to an online movement in 2012 coincided with a shift in social media activity. Analysis of Facebook activity suggests a move from functional to anatomical hierarchy and a corresponding move from many-to-many communication to one-to-many communication. In conclusion, we argue that this development served to undermine the movement's anarchist principles of organization.     

英汉省略现象探析

省略是英汉语中共有的语言现象,它是语篇衔接和连贯的重要手段之一。本文结合系列语料实例,就英汉语中的省略现象进行了对比分析与探讨,为学习者在语言交际活动中正确理解和恰当运用省略提供一定的参考和启示。     

肢体语言在跨文化交际中的应用

肢体语言与外语一样,都是文化的一部分。除一些世界公认的肢体语言外,不同的文化还有各自的肢体语言。跨文化交际时,相同的肢体语言形式可能具有完全不同的意义。简单对比中美肢体语言的差别,同时它的交际作用也不能忽视。肢体语言使用的好,可以取得较好的交际效果。反之,会陷入交际误区,影响交际。在跨文化交际中,要学习和了解肢体语言在不同国家和地区的含义和运用。     

Retweets of officials’ alarming vs reassuring messages during the COVID-19 pandemic: Implications for crisis management

Coronavirus related discussions have spiraled at an exponential rate since its initial outbreak. By the end of May, more than 6 million people were diagnosed with this infection. Twitter witnessed an outpouring of anxious tweets through messages associated with the spread of the virus. Government and health officials replied to the troubling tweets, reassuring the public with regular alerts on the virus's progress and information to defend against the virus. We observe that social media users are worried about Covid 19-related crisis and we identify three separate conversations on virus contagion, prevention, and the economy. We analyze the tone of officials’ tweet text as alarming and reassuring and capture the response of Twitter users to official communications. Such studies can provide insights to health officials and government agencies for crisis management, specifically regarding communicating emergency information to the public via social media for establishing reassurance.     

A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection

Hate speech is an increasingly important societal issue in the era of digital communication. Hateful expressions often make use of figurative language and, although they represent, in some sense, the dark side of language, they are also often prime examples of creative use of language. While hate speech is a global phenomenon, current studies on automatic hate speech detection are typically framed in a monolingual setting. In this work, we explore hate speech detection in low-resource languages by transferring knowledge from a resource-rich language, English, in a zero-shot learning fashion. We experiment with traditional and recent neural architectures, and propose two joint-learning models, using different multilingual language representations to transfer knowledge between pairs of languages. We also evaluate the impact of additional knowledge in our experiment, by incorporating information from a multilingual lexicon of abusive words. The results show that our joint-learning models achieve the best performance on most languages. However, a simple approach that uses machine translation and a pre-trained English language model achieves a robust performance. In contrast, Multilingual BERT fails to obtain a good performance in cross-lingual hate speech detection. We also experimentally found that the external knowledge from a multilingual abusive lexicon is able to improve the models’ performance, specifically in detecting the positive class. The results of our experimental evaluation highlight a number of challenges and issues in this particular task. One of the main challenges is related to the issue of current benchmarks for hate speech detection, in particular how bias related to the topical focus in the datasets influences the classification performance. The insufficient ability of current multilingual language models to transfer knowledge between languages in the specific hate speech detection task also remain an open problem. However, our experimental evaluation and our qualitative analysis show how the explicit integration of linguistic knowledge from a structured abusive language lexicon helps to alleviate this issue.