Trump’s Twitter Propaganda During Covid-19

<em><span lang="EN-US"><em><span>During the early Covid-19 pandemic, led by controversial presidential figure Donald Trump, the US seemed to be overwhelmed by this microbial creature, proving to be one of the countries with the most Covid-19. Besides health impacts, there are many multi-effects afterward, such as economic, social, political, and so on, that must be faced after this Covid-19 outbreak. Moreover, the US will hold a presidential election in November 2020. This challenge makes Trump must focus on how to complete Covid-19 while maintaining electability as President. One of the methods adopted is by forming narration through Twitter. Donald Trump is an active Twitter user who often tweets about his stance. Therefore, this study wants to analyze Trump's Twitter tweet content based on propaganda based on six propaganda classifications based on Holly Thayer's Theory. The quantitative content analysis method is the systematic and replicable examination of symbols of communication. The object of research in this article is Twitter's @realDonaldTrump tweet. We analyzed Donald Trump's Twitter content in the period 1 March 2020-27 May 2020 with a systematic random sample method. Our result shows that Trump constructs a message to support his policy and maintain his electability.</span></em> </span></em>

Debates about vaccines and climate change on social media networks: a study in contrasts

Vaccines and climate change have much in common. In both cases, a scientific consensus contrasts with a divided public opinion. They also exemplify coupled human–environment systems involving common pool resources. Here we used machine learning algorithms to analyze the sentiment of 87 million tweets on climate change and vaccines in order to characterize Twitter user sentiment and the structure of user and community networks. We found that the vaccine conversation was characterized by much less interaction between individuals with differing sentiment toward vaccines. Community-level interactions followed this pattern, showing less interaction between communities of opposite sentiment toward vaccines. Additionally, vaccine community networks were more fragmented and exhibited numerous isolated communities of neutral sentiment. Finally, pro-vaccine individuals overwhelmingly believed in anthropogenic climate change, but the converse was not true. We propose mechanisms that might explain these results, pertaining to how the spatial scale of an environment system can structure human populations.

Assessment of Antipsychotic Medications on Social Media: Machine Learning Study

Background: Antipsychotic medications are the first-line treatment for schizophrenia. However, non-adherence is frequent despite its negative impact on the course of the illness. In response, we aimed to investigate social media posts about antipsychotics to better understand the online environment in this regard.Methods: We collected tweets containing mentions of antipsychotic medications posted between January 1st 2019 and October 31st 2020. The content of each tweet and the characteristics of the users were analyzed as well as the number of retweets and likes generated.Results: Twitter users, especially those identified as patients, showed an interest in antipsychotic medications, mainly focusing on the topics of sexual dysfunction and sedation. Interestingly, paliperidone, despite being among one of the newest antipsychotics, accounted for a low number of tweets and did not generate much interest. Conversely, retweet and like ratios were higher in those tweets asking for or offering help, in those posted by institutions and in those mentioning cognitive complaints. Moreover, health professionals did not have a strong presence in tweet postings, nor did medical institutions. Finally, trivialization was frequently observed.Conclusion: This analysis of tweets about antipsychotic medications provides insights into experiences and opinions related to this treatment. Twitter user perspectives therefore constitute a valuable input that may help to improve clinicians' knowledge of antipsychotic medications and their communication with patients regarding this treatment.

Budaya komunikasi virtual di Twitter dan Tiktok: Perluasan makna kata estetik

Kata estetik mengalami perluasan makna dari makna asalnya akibat penggunaannya di Twitter dan TikTok. Perluasan makna itu menimbulkan perdebatan pada pengguna kedua media sosial tersebut. Studi ini bertujuan untuk mengetahui penyebab dan proses terjadinya perluasan makna, termasuk motivasi pengguna Twitter dan TikTok menggunakan kata estetik yang maknanya berbeda dengan Kamus Besar Bahasa Indonesia (KBBI). Studi berjenis kualitatif ini menggunakan pendekatan etnografi virtual dengan mewawancarai tujuh informan pengguna Twitter dan TikTok. Empat orang informan pengguna Twitter, yaitu @iyayaudahgpp, @semanismimpimu, @s8joh, dan @CharmingDevilll. Tiga orang informan pengguna TikTok, yaitu @Wiyantika, @aqua.ush, dan @pcynjm131. Hasil penelusuran virtual menunjukkan bahwa kata estetik telah mengalami perluasan makna. Kata estetik tidak sekadar bermakna indah tetapi juga berarti ungkapan kelucuan, ekspresi sindiran, dan pujian penampilan fisik. Penyebab perluasan makna adalah penggunaan kata estetik pada caption, cuitan, komentar, dan tagar oleh pengguna Twitter dan TikTok. Motivasi penggunaan kata estetik, disebabkan mengikuti tren, keisengan, dan candaan. Budaya berkomunikasi di media sosial berperan penting dalam perluasan makna kata dan penciptaan kata-kata baru. The word aesthetic has expanded from its original meaning due to its use on Twitter and TikTok. The expansion of meaning has caused debate among users of the two social media. This study aims to determine the causes and processes of the expansion of meaning, including Twitter and TikTok users' motivation to use aesthetic words that have different meanings from the Indonesian Dictionary (KBBI). The type of research is qualitative with a virtual ethnographic approach by interviewing seven informants using Twitter and TikTok. Four Twitter user informants, namely @iyayaudahgpp, @semanismimpimu, @s8joh, and @CharmingDevilll. Three informants using TikTok, namely @Wiyantika, @aqua.ush, and @pcynjm131. The virtual search results show that the word aesthetic has expanded its meaning. The word aesthetic does not only mean beautiful but also means an expression of cuteness, an expression of satire, and a compliment of physical appearance. The cause of the expansion of meaning is the use of aesthetic words in captions, tweets, comments, and hashtags by Twitter and TikTok users. The motivation for using the word aesthetic is due to following trends, fads, and jokes. The culture of communicating on social media has an essential role in expanding the meaning of words and creating new words.

SENTIMENT ANALYSIS OF CURRENT TRENDING TOPICS ON TWITTER USER BASE

Twitter has now become the most common social platform to express views on any topic. A micro-blogging social media offers a way for people around the world to show their sentiments about any political, social and cultural subject of the time. In this paper, the sentimental analysis approach has been used to analyze the positive and negative sentiments of Twitter users about some top trending #tags around the globe. The data has been collected between the duration of March to April 2021. The collected data were processed by using the Python program and then transformed our data set with the help of the SQL database. We have used graphs and tables to present the data, collected under three hashtags; which were top trending topics on that particular era. The tweets were elaborated by positive, negative and neutral sentiments which were depicted in graphs. It is clear from the results and comparison that social media has a strong influence in the present era and can be highly helpful to use as a predictor of any political, social situation prevailing in any country or worldwide. It has also been helpful for business communities to analyze their products in the same manner to improve their business growth.

Why should you respond to customer complaints on a personal level? The silent observer's perspective

PurposeThis paper analyzes the effect of company response style and complaint source on silent observers' reactions to a service failure episode vented on Twitter.Design/methodology/approachIn a 2 × 2 experimental design, company response style (personalized vs automatic) and complaint source (ordinary Twitter user vs influencer) were manipulated to test the hypotheses.FindingsComplaint source moderates the effect of company response style on brand image, purchase intention and electronic word-of-mouth (eWOM). Thus, the authors found that a personalized response to a complaint, compared to an automatic response, leads to a more favorable brand image as well as purchase intention and eWOM intention when the complainant is an ordinary Twitter user. However, the automatic response, compared to the personalized one, is better perceived when the complainant is an influencer. The authors also found that service failure response attribution and the emotions elicited during the firm–complainant interaction mediate the previous effects.Research limitations/implicationsThis paper deals with the company's initial reaction after a complaint is posted on Twitter; however, the complaint-handling process is longer, and both the customer and silent observers await a resolution. Future research could tackle subsequent stages of the process and different recovery strategies.Practical implicationsThe study offers meaningful insights regarding complaint handling on Twitter and how the effectiveness of the company response style depends on the complaint source. Marketers should offer adapted personalized responses to prompt positive behavioral intentions for ordinary Twitter users, who represent prospective consumers. However, a personalized response given to an influencer may be perceived more negatively, as silent observers may interpret that the company offers such a response just because the complaint comes from a well-known person who can reach many users and not because of an honest interest in serving consumers.Originality/valueThis research focuses on the underresearched area of the impact of online complaints on silent observers, a large group of prospective consumers quietly exposed to complaints aired on Twitter. The underlying mechanisms are also identified.

Interpretation of Dear Me Beauty Product Rebranding: Twitter Users Analysis

This study aimed to discuss the way Dear Me Beauty was rebranding their brand by launching new products after they have a brand crisis. The data of this research was obtained from the comment of a Twitter user. This study used the encoding decoding theory from Stuart Hall. The findings of this study was the response from Twitter users was positive, implying that Dear Me Beauty's rebranding was successful. The researchers discovered that the messages from Dear Me Beauty conveyed through their newest product could be well interpreted and accepted by the public.