Exploring user sentiment towards sponsorship and ambush marketing

2019 ◽  
Vol 20 (4) ◽  
pp. 583-602 ◽  
Author(s):  
Nick Burton
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Value Added ◽  
Statistical Modelling ◽  
Consumer Attitudes ◽  
Content Type ◽  
Event Marketing ◽  
Ambush Marketing ◽  
Ethics Research ◽  
Twitter Users

Purpose The purpose of this paper is to explore consumer attitudes towards ambush marketing and official event sponsorship through the lens of sentiment analysis, and to examine social media users' ethical responses to digital event marketing campaigns during the 2018 FIFA World Cup. Design/methodology/approach The study employed a sentiment analysis, examining Twitter users’ utilization of sponsor and non-sponsor promotional hashtags. Statistical modelling programme R was used to access Twitter’s API, enabling the analysis and coding of user tweets pertaining to six marketing campaigns. The valence of each tweet – as well as the apparent user motivation underlying each post – was assessed, providing insight into Twitter users’ ethical impressions of sponsor and ambush marketer activities on social media and online engagement with social media marketing. Findings The study’s findings indicate that consumer attitudes towards ambush marketing may be significantly more positive than previously thought. Users’ attitudes towards ambush marketing appear significantly more positive than previously assumed, as users of social media emerged as highly responsive to creative and value-added non-sponsor campaigns. Originality/value The findings affirm that sentiment analysis may afford scholars and practitioners a viable means of assessing consumer attitudes towards social marketing activations, dependent upon campaign objectives and strategy. The study provides a new and invaluable context to consumer affect and ambush ethics research, advancing sponsorship and ambush marketing delivery and social sponsorship analytical practice.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

Analyzing Twitter to explore perceptions of Asian restaurants

2016 ◽  
Vol 7 (4) ◽  
pp. 405-422 ◽  
Author(s):  
Seunghyun Brian Park ◽  
Jichul Jang ◽  
Chihyung Michael Ok
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Text Messages ◽  
Media Analysis ◽  
Food Culture ◽  
Social Media Analytics ◽  
Emotional States ◽  
Content Type ◽  
Different Types ◽  
Twitter Users

Purpose The purpose of this paper is to use Twitter analysis to explore diner perceptions of four types of Asian restaurants (Chinese, Japanese, Korean and Thai). Design/methodology/approach Using 86,015 tweets referring to Asian restaurants, this research used text mining and sentiment analysis to find meaningful patterns, popular words and emotional states in opinions. Findings Twitter users held mingled perceptions of different types of Asian restaurants. Sentiment analysis and ANOVA showed that the average sentiment scores for Chinese restaurants was significantly lower than the other three Asian restaurants. While most positive tweets referred to food quality, many negative tweets suggested problems associated with service quality or food culture. Research limitations/implications This research provides a methodology that future researchers can use in applying social media analytics to explore major issues and extract sentiment information from text messages. Originality/value Limited research has been conducted applying social media analysis in hospitality research. This study fills a gap by using social media analytics with Twitter data to examine the Twitter users’ thoughts and emotions for four different types of Asian restaurants.

scholarly journals Social media and sentiment in bioenergy consultation

2016 ◽  
Vol 10 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Victoria Uren ◽  
Daniel Wright ◽  
James Scott ◽  
Yulan He ◽  
Hassan Saif
Keyword(s):  
Social Media ◽  
New Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Public Consultation ◽  
Short Paper ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

What the fake? Assessing the extent of networked political spamming and bots in the propagation of #fakenews on Twitter

2019 ◽  
Vol 43 (1) ◽  
pp. 53-71 ◽  
Author(s):  
Ahmed Al-Rawi ◽  
Jacob Groshek ◽  
Li Zhang
Keyword(s):  
Social Media ◽  
Data Sets ◽  
Fake News ◽  
Content Type ◽  
Mainstream Media ◽  
News Discourse ◽  
Twitter Data ◽  
News Organizations ◽  
Twitter Users ◽  
One Year

PurposeThe purpose of this paper is to examine one of the largest data sets on the hashtag use of #fakenews that comprises over 14m tweets sent by more than 2.4m users.Design/methodology/approachTweets referencing the hashtag (#fakenews) were collected for a period of over one year from January 3 to May 7 of 2018. Bot detection tools were employed, and the most retweeted posts, most mentions and most hashtags as well as the top 50 most active users in terms of the frequency of their tweets were analyzed.FindingsThe majority of the top 50 Twitter users are more likely to be automated bots, while certain users’ posts like that are sent by President Donald Trump dominate the most retweeted posts that always associate mainstream media with fake news. The most used words and hashtags show that major news organizations are frequently referenced with a focus on CNN that is often mentioned in negative ways.Research limitations/implicationsThe research study is limited to the examination of Twitter data, while ethnographic methods like interviews or surveys are further needed to complement these findings. Though the data reported here do not prove direct effects, the implications of the research provide a vital framework for assessing and diagnosing the networked spammers and main actors that have been pivotal in shaping discourses around fake news on social media. These discourses, which are sometimes assisted by bots, can create a potential influence on audiences and their trust in mainstream media and understanding of what fake news is.Originality/valueThis paper offers results on one of the first empirical research studies on the propagation of fake news discourse on social media by shedding light on the most active Twitter users who discuss and mention the term “#fakenews” in connection to other news organizations, parties and related figures.

Exploring newsjacking as social media–based ambush marketing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nicholas Burton ◽  
Cole McClean
Keyword(s):  
Social Media ◽  
Structural Characteristics ◽  
Marketing Research ◽  
Sport Marketing ◽  
Media Analysis ◽  
Sporting Events ◽  
Content Type ◽  
Strategic Intent ◽  
Ambush Marketing ◽  
Winter Olympic Games

PurposeThis study explores the use of event-related promotional hashtags by non-sponsors as a form of social ambushing, akin to newsjacking, as potential means of ambushing major events and the potential challenges facing commercial rights holders.Design/methodology/approachFramed within the context of the 2018 PyeongChang Winter Olympic Games, the present research takes a descriptive analytical approach to social media analysis. Social media data were accessed from Twitter's API across a six-week Games period and subsequently coded and categorized based upon strategic intent, content and key structural characteristics. A quantitative analysis of Tweet distribution, frequency and buzz was then conducted, providing insight into the impacts and effects of social ambushing via newsjacking.FindingsImportantly, the study's findings suggest that whilst newsjacking by non-sponsors throughout the Games was pervasive, the potential reach and impact of such social ambushing may be limited. Non-sponsoring firms primarily adopted Games hashtags for behavioural or diversionary means, however consumer response to such attempts was minimal. These findings offer renewed perspective for scholars and practitioners on social ambushing and ambush marketing interventionism.Originality/valueThis research provides an important investigation into the manifestations and potential implications of social ambushing and illustrates the potential for brands to newsjack sporting events through unauthorized hashtag usage, necessary advances in sport marketing research.

Sentiment search: an emerging trend on social media monitoring systems

2014 ◽  
Vol 66 (5) ◽  
pp. 553-580 ◽  
Author(s):  
Tung Thanh Nguyen ◽  
Tho Thanh Quan ◽  
Tuoi Thi Phan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Data ◽  
Content Type ◽  
Social Monitoring ◽  
Cross Domain ◽  
Linguistic Rules ◽  
Domain Ontologies ◽  
Media Monitoring ◽  
Media Channels

Purpose – The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion. Design/methodology/approach – The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains. Findings – The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques. Research limitations/implications – The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks. Originality/value – The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

scholarly journals Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data (Preprint)

2020 ◽  
Author(s):  
Yankun Gao ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Viral Infections ◽  
Application Programming Interface ◽  
Electronic Cigarette ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Twitter Users ◽  
Public Health Practitioners

BACKGROUND Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. OBJECTIVE In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. METHODS The study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. RESULTS The US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

scholarly journals Analysis of User Sentiment of Twitter to Draft KUHP

2020 ◽  
Vol 16 (3) ◽  
pp. 273
Author(s):  
Nawang Indah Cahyaningrum ◽  
Danty Welmin Yoshida Fatima ◽  
Wisnu Adi Kusuma ◽  
Sekar Ayu Ramadhani ◽  
Muhammad Rizqi Destanto ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
The Other ◽  
Mining Methods ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Bahasa Indonesia

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.

Social media content and sentiment analysis on consumer security breaches

2016 ◽  
Vol 23 (4) ◽  
pp. 855-869 ◽  
Author(s):  
Jianqiang Hao ◽  
Hongying Dai
Keyword(s):  
Social Media ◽  
Empirical Study ◽  
Sentiment Analysis ◽  
Real Time ◽  
Public Perception ◽  
Public Awareness ◽  
Content Type ◽  
Security Breaches ◽  
Short Period ◽  
Significant Difference

Purpose Security breaches have been arising issues that cast a large amount of financial losses and social problems to society and people. Little is known about how social media could be used a surveillance tool to track messages related to security breaches. This paper aims to fill the gap by proposing a framework in studying the social media surveillance on security breaches along with an empirical study to shed light on public attitudes and concerns. Design/methodology/approach In this study, the authors propose a framework for real-time monitoring of public perception to security breach events using social media metadata. Then, an empirical study was conducted on a sample of 1,13,340 related tweets collected in August 2015 on Twitter. By text mining a large number of unstructured, real-time information, the authors extracted topics, opinions and knowledge about security breaches from the general public. The time series analysis suggests significant trends for multiple topics and the results from sentiment analysis show a significant difference among topics. Findings The study confirms that social media monitoring provides a supplementary tool for the traditional surveys which are costly and time-consuming to track security breaches. Sentiment score and impact factors are good predictors of real-time public opinions and attitudes to security breaches. Unusual patterns/events of security breaches can be detected in the early stage, which could prevent further destruction by raising public awareness. Research limitations/implications The sample data were collected from a short period of time on Twitter. Future study could extend the research to a longer period of time or expand key words search to observe the sentiment trend, especially before and after large security breaches, and to track various topics across time. Practical implications The findings could be useful to inform public policy and guide companies responding to consumer security breaches in shaping public perception. Originality/value This study is the first of its kind to undertake the analysis of social media (Twitter) content and sentiment on public perception to security breaches.

scholarly journals Company Generated Problem‐Solving Content on Social Media (SM) and Online Brand Equity: Designing and Testing a Model for Its Effectiveness

2019 ◽  
Author(s):  
Tali Rezun
Keyword(s):  
Social Media ◽  
Problem Solving ◽  
Brand Equity ◽  
Model Development ◽  
Value Added ◽  
Empirical Literature ◽  
Online Data ◽  
Content Type ◽  
Main Research ◽  
Positive Effect

Based on 15 years of online experience and research dealing with SEO, social media and brand positioning the doctorate dissertation emerged. Doctorate dissertation (DBA) titled: “Company Generated Problem-Solving Content on Social Media (SM) and Online Brand Equity: Designing and Testing a Model for Its Effectiveness” deals with online model development using professional value added content with a purpose to aid small and mid-sized enterprises promote their brands online. The four year research period from 2012 to 2016, followed company “Naton HR” as the main research subject using dissertation model to position its brand online. Data was collected and the model confirmed. The importance of professional content and its positive effect on brand visibility and business performance has become an exceedingly important marketing issue. To date, there has been limited empirical literature on professional “content type” in social media (SM). However, there is consensus among researchers that content has a positive effect on brand exposure, and, in fact, results in improved company sales.<br>

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