Customer communication in twitter

Juniorprofessur fürKommunikations- undKollaborationsmanagement

Prof. Dr. Stefan Stieglitzwww.wi-kuk.de

KuKWIRTSCHAFTS INFORMATIK

Customer Communication in Twitter

A case study of Toyota in a crisis

Stefan StieglitzNina Krüger

Linh Dang-Xuan

DIATA ´11

Juniorprofessur fürKommunikations- undKollaborationsmanagement(Prof. Dr. Stefan Stieglitz)Ku

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WIRTSCHAFTS INFORMATIK

2Customer Communication in Twitter

Agenda

Motivation and Background

Related Work

Research Design

Summary

Research Approach for the further study


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Social Media

Change in Public CommunicationOrganization (enterprise, political player, …)

Media (journalists as gatekeepers)Agenda Setting

Public Relations Public one-way communication

Customers, Citizens

Social Media marketing

Public Feedback, Questions, opinions, customer innovation

Communication between social media users• Complains• Trends etc.

Ideas, Innovations, opinions, Trends, complains, recommendations, etc…

Viral marketing, undercover actions

Media Monitoring


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What’s the difference?Opinion leaders are hard to identify

Much more data and richer information

Possibility to track data automatically and analyze them (digital information, ...) very fast (e.g. direct feedback on campaigns)

Everybody has a voice – risks and chances for companies

Long tail – opinion gathering

Choice of words – no strict rules like in press releases, different styles because of different platforms


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Research QuestionsGoals: getting a deeper understanding about the dynamics of the structures of

communication, the participation of the stakeholder and their sentiments in the communication.

1. Are crisis-related issues in twitter discussed (like in the classic media) and are these discussions characterized by peaks and buzzing-stages? Do involved user post higher frequented in peaks than in buzzing stages?

2. Are the postings in the peaks filled with more sentiment-words than in the buzzing stages?

3. Is there a difference between sentiments in Tweets created by power-tweeters (PT) only and the sentiments in Tweets created by all participants of the sample?


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Agenda


Related Work

Research Design

Summary



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Sentiment in Twitter Messages

Sentiment Analysis

• Sentiment analysis of Tweets: Events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood (Bollen et al., 2009).

• Link measures of public opinion derived from polls to sentiment measured from Twitter messages: Sentiment word frequencies in contemporaneous Twitter messages do correlate with several public opinion time series such as surveys on consumer confidence and political opinion over the 2008 to 2009 period (O’Connor et al., 2010).

• Study of political tweets around the 2009 German federal election: Tweet sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters’ political preferences (Tumasjan et al., 2010).


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Agenda


Related Work

Research Design

Summary



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ProceedingObjects of study: the Top10 players in the automotive industry

Identification of appropriate keywords using classic print media:•Identification of keywords by scanning the New York Times over a periode of two weeks,

analyzing these articles which are related to one of the carmakers.

Structural analysis of the course topics: •Observation, analysis and documentation of public communication in

Twitter using the keywords found with the help of a software prototype•Cleaning up the data


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Case selection

Identification of an issue•The large-scale car recall due to a technical fault in the gas pedals and the breaks

Using the keyword-combination „recall/-s“, „Toyota“

Implementation of the Issue Scanning for the periode 13-31 calendarweek:•732.003 Tweets: „Toyota“•37.232 Tweets: „recall“ und „Toyota“


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Outline data

5.870 Tweets with Hashtags (1.896 #toyota, 851 #recall) (16%)

3.190 Tweets with linked URLs (8,6%)

Relatively uniform distribution of users involved in the communication•The 10 most active Twitter accounts did 6.237 postings all in all (17,5 % of all Tweets)•Most active account: 1.237 Tweets (Toyota_recall)•The two identified official Toyota-account published only 237 and 164 Tweets


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Findings


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Sentiment Analysis

• Classifying the polarity of a given text at the document, sentence, or feature/aspect level

• Linguistic dimensions- Positive emotions (positive feelings, optimism)- Negative emotions (anger, anxiety, sadness)

• Example: Creating sentiment profile for companies, parties or affiliated individuals (e.g., in the form of positive/negative-emotion scales)


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Findings

Uniform percentage of sentiment words in the discussion

A clear tendency of a stronger polarization in peaks


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Account name Neutral Tweets Tweets with sentiment words

Toyota_recall 71,2 % 28,8 %

Toy_Yoda 79,5 % 20,5 %

toyotacomplaint 70,6 % 29,4 %

Toyotalinks 66,6 % 33,4 %

Toyotadispatch 69,2 % 30,8 %

Allairbagrecall 11,1 % 88,9 %

Prius_Bat_Recon 20,5 % 79,5 %

JaniceChase 85,5 % 14,5 %

Kulchawheels 55,4 % 44,6 %

VehixCar 60,4 % 39,6 %

Findings

Is there a difference between sentiments in Tweets created by power-tweeters (PT) only and the sentiments in Tweets created by all participants of the sample?


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Agenda


Research Approaches

Related Work

Summary



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SummaryOrganization-related issues are discussed in Twitter

Using the issue scanning keywords can identify topics for tracking dynamics

In crisis situations, more individuals participate in the discussion (the contribution per user does not rise)

In peak periods, there are clear trends in the discussion to positive or negative sentiments

Measures may differ in different types of discussion


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Agenda


Related Work

Research Design

Summary

Research Approaches for the further study



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Further Research

• Research on dynamics of specific topics in social networks

• Comparative studies of different cases

• Content analysis of the Tweets

• Social Network analysis



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Many thanks for your attention!


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Universität MünsterInstitut für Wirtschaftsinformatik

Juniorprofessur für Kommunikations- undKollaborationsmanagement

Leonardo-Campus 3D-48149 Münster

http://www.wi.uni-muenster.de/kuk

Kontakt

2111.04.2023

Nina Krüger M.A.

[email protected] 83 38 014

Customer communication in twitter

Technology

Transcript of Customer communication in twitter