User and Firm Generated Content on Online Social Media

2016 ◽  
Vol 6 (3) ◽  
pp. 34-49 ◽  
Author(s):  
Abhinita Daiya ◽  
Subhadip Roy
Keyword(s):  
Social Media ◽  
Levels Of Analysis ◽  
Future Research ◽  
Media Content ◽  
Media Communication ◽  
Communication Content ◽  
Online Social Media ◽  
Research Questions ◽  
Social Media Communication ◽  
Level Of Analysis

Social media communication content has gained a lot of interest in e-commerce literature. The present research note explores the scope of social media communication content across content source and levels of analysis. Based on a comprehensive review of 36 empirical papers spanning a decade (2004-2016), the research in social media content source is divided as user generated and firm generated. The levels of analysis are divided into three groups: users and society, platforms and intermediaries and firms and industries. Subsequently, a grid with six cells is created that has the content source (user/firm) on one axis and level of analysis on the other. The findings reveal communication content across users and society to be the most researched area, whereas, platforms and intermediaries being the least researched. Further, a set of future research questions are proposed for content in social media across various levels of analysis.

scholarly journals When Nobody “Likes” You: Perceived Ostracism Through Paralinguistic Digital Affordances Within Social Media

2018 ◽  
Vol 4 (3) ◽  
pp. 205630511880030 ◽  
Author(s):  
Rebecca A. Hayes ◽  
Eric D. Wesselmann ◽  
Caleb T. Carr
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Communication Channels ◽  
Psychological Effects ◽  
Future Research ◽  
Media Content ◽  
Media Communication ◽  
Social Media Platforms ◽  
The One ◽  
Social Media Communication

This research explores the processes of perceived ostracism ensuing from a lack of feedback via paralinguistic digital affordances (PDAs), the one-click tools (e.g., Likes and +1s) which are one of the most used features of social media, provided to an individual’s posted social media content. The positive and negative psychological outcomes of social media communication have been well-documented. However, as social media have become entrenched as some of our most common communication channels, the absence of communication via social media has been underexplored and may have negative psychological and communicative outcomes. We utilized focus groups ( N = 37) to examine perceptions of ostracism when individuals did not receive PDAs to their posted content across social media platforms. Participants reported feeling excluded only when they did not receive PDAs from select relationally close or socially superior network members, suggesting audience targeting and expectations when posting. Users frequently attributed low PDA counts to system and content factors. These results contribute to a developing understanding of the psychological effects of lack of communication via social media and provide insight for future research, demonstrating that social exclusion may not manifest from a complete lack of social interaction but rather may occur when individuals do not receive expected or desired feedback.

scholarly journals CHALLENGING SPATIAL MARGINALIZATION THROUGH SOCIAL MEDIA COMMUNICATION? A CASE STUDY OF THE TWITTER DISCOURSE ON HOUSING IN BERLIN

2020 ◽  
Author(s):  
Daniela Stoltenberg
Keyword(s):  
Social Media ◽  
Social Stratification ◽  
Public Discourse ◽  
Social Power ◽  
Spatial Inequality ◽  
Future Research ◽  
Power Structures ◽  
Media Communication ◽  
Urban Issues ◽  
Social Media Communication

Urban public life has historically and famously been structured by social stratification and a segregation of social milieus. Such spatialized social inequality along the lines of, most importantly, class, age, and ethnicity engenders unequal access to civic participation and supportive social networks. Meanwhile, the Internet and Web 2.0 technologies in particular have often been hailed for their potential of bringing underrepresented voices into the public discourse and even creating so-called “networked counterpublics”, challenging social power structures. This contribution seeks to address the question of whether social media communication about urban issues challenges or reproduces patterns of spatial inequality in its attention distribution. Empirically, it investigates the distribution of place-naming within the Berlin-based Twitter discourse on housing. It finds that - while issue attention in the urban Twitter discourse is clearly spatially unequal, with a striking imbalance between center and periphery - neither sociodemographic composition nor issue characteristics perform well in explaining these patterns. Instead it proposes focusing more on local civic and activist infrastructure in future research.

Topics and topical phases in German social media communication during a disaster

2018 ◽  
Vol 24 (2) ◽  
pp. 221-264 ◽  
Author(s):  
SABINE GRÜNDER-FAHRER ◽  
ANTJE SCHLAF ◽  
GREGOR WIEDEMANN ◽  
GERHARD HEYER
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Disaster Management ◽  
Language Processing ◽  
Topic Model ◽  
Model Analysis ◽  
Media Content ◽  
Media Communication ◽  
Social Media Communication

AbstractSocial media are an emerging new paradigm in interdisciplinary research in crisis informatics. They bring many opportunities as well as challenges to all fields of application and research involved in the project of using social media content for an improved disaster management. Using the Central European flooding 2013 as our case study, we optimize and apply methods from the field ofnatural language processingand unsupervised machine learning to investigate the thematic and temporal structure of German social media communication. By means of topic model analysis, we will investigate which kind of content was shared on social media during the event. On this basis, we will, furthermore, investigate the development of topics over time and apply temporal clustering techniques to automatically identify different characteristic phases of communication. From the results, we, first, want to reveal properties of social media content and show what potential social media have for improving disaster management in Germany. Second, we will be concerned with the methodological issue of finding and adapting natural language processing methods that are suitable for analysing social media data in order to obtain information relevant for disaster management. With respect to the first, application-oriented focal point, our study reveals high potential of social media content in the factual, organizational and psychological dimension of the disaster and during all stages of the disaster management life cycle. Interestingly, there appear to be systematic differences in thematic profile between the different platforms Facebook and Twitter and between different stages of the event. In context of our methodological investigation, we claim that if topic model analysis is combined with appropriate optimization techniques, it shows high applicability for thematic and temporal social media analysis in disaster management.

scholarly journals Statistical Properties of a New Social Media Context Awareness Scale (SMCA)—A Preliminary Investigation

2020 ◽  
Vol 12 (12) ◽  
pp. 5201
Author(s):  
Dana Rad ◽  
Valentina Balas ◽  
Ramona Lile ◽  
Edgar Demeter ◽  
Tiberiu Dughi ◽  
...  
Keyword(s):  
Social Media ◽  
Media Literacy ◽  
Context Awareness ◽  
Preliminary Investigation ◽  
Communication Process ◽  
Media Content ◽  
Convenience Sample ◽  
Media Communication ◽  
Process Understanding ◽  
Social Media Communication

In the Internet of Things era, or in the digitalization and mediatization of everything paradigm, where context awareness computing is on the rise, people are also facing a new challenge, that of being aware of the digital contexts, in all situations when surfing the internet’s ocean of row information. The emerging social media context awareness competency refers to a new emerging skill regarding the trust load people give to a specific social media context they encounter. Since it is an emergent competence, it cannot be understood as standalone. If the digital context would not be available, we would not develop such a competence. Being a competence, it must be defined by three core elements: Knowledge, skills, and attitudes. Consequently, we have operationalized the competence of social media context awareness in terms of social media literacy, social media communication process understanding, social media content impact awareness, and social media confidence. An online questionnaire was created under the Erasmus+ project Hate’s Journey, addressing a convenience sample of 206 online youth respondents from Turkey, Spain, Latvia, and Romania. Our team has computed a reliability analysis on the social media context awareness scale designed with four items referring to social media literacy (m = 3.79, SD = 1), social media communication process understanding (m = 3.77, SD = 0.9), social media content impact awareness (m = 3.88, SD = 1), and social media confidence (m = 3.45, SD = 1). Cronbach’s alpha coefficient and the Exploratory Factor Analysis demonstrated the acceptable reliability of the SMCA scale, α = 0.87. Conclusions, implications, and limitations are discussed in the context of social sustainability.

scholarly journals Emoji Identification and Prediction in Hebrew Political Corpus

10.28945/4346 ◽  
2019 ◽  
Keyword(s):  
Social Media ◽  
Text Message ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Media Communication ◽  
Prediction Task ◽  
Morphologically Rich Languages ◽  
Definition Of ◽  
Textual Content ◽  
Social Media Communication

[This Proceedings paper was revised and published in the 2019 issue of the journal Issues in Informing Science and Information Technology, Volume 16] Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning (ML) approach. We compare two text representation approaches, i.e., n-grams and character n-grams and analyze the contribution of additional metadata features to the classification. Contribution: The task of emoji identification is novel. We extend the definition of the emoji prediction task by allowing to use not only the textual content but also meta-data analysis. Findings: Metadata improve the classification accuracy in the task of emoji identification. In the task of emoji prediction it is better to apply feature selection. Recommendations for Practitioners: In many of the cases the classifier decision seems fitter to the comment con-tent than the emoji that was chosen by the commentator. The classifier may be useful for emoji suggestion. Recommendations for Researchers: Explore character-based representations rather than word-based representations in the case of morphologically rich languages. Impact on Society: Improve the modeling of social media communication. Future Research: We plan to address the multi-label setting of the emoji prediction task and to investigate the deep learning approach for both of our classification tasks.

scholarly journals Citizen Coproduction and Social Media Communication: Delivering a Municipal Government’s Urban Services through Digital Participation

2021 ◽  
Vol 11 (2) ◽  
pp. 59
Author(s):  
Wonhyuk Cho ◽  
Winda Dwi Melisa
Keyword(s):  
Social Media ◽  
Municipal Government ◽  
Media Content ◽  
Media Content Analysis ◽  
Dominant Form ◽  
Media Communication ◽  
Twitter Account ◽  
Media Platform ◽  
Media Communications ◽  
Social Media Communication

This study investigated how social media is used by a municipal government agency for communication of citizen coproduction initiatives, through social media content analysis of the government’s official Twitter account. This article identified that the dominant form of social media coproduction in the Bandung municipal government in Indonesia is government-to-citizen (G2C) interaction, focused primarily on informing and nudging (86.62%) citizens, as well as some limited elements of citizen-to-government (C2G) communication, such as citizen sourcing and citizen reporting (8.96%). The municipal government uses various visual tools on Twitter to disseminate G2C information and convey its messages. Regarding the phase of the service cycle, this study found that the majority of social media communications are related to co-assessment (52.26%) and co-designing (42.24%), with a limited number of tweets about co-delivery (3.25%). Based on these findings, this article discusses the shifting relationship between government and citizens brought on by the adoption of this social media platform in its service delivery arrangement.

Food retailing marketing management: social media communication for healthy food

2019 ◽  
Vol 47 (9) ◽  
pp. 928-956 ◽  
Author(s):  
Antonella Samoggia ◽  
Aldo Bertazzoli ◽  
Arianna Ruggeri
Keyword(s):  
Social Media ◽  
Food Products ◽  
Healthy Food ◽  
Marketing Management ◽  
Management Approach ◽  
Media Communication ◽  
Content Type ◽  
Communication Content ◽  
Use Of Social Media ◽  
Social Media Communication

Purpose Healthy food sales have increased in recent decades. Retailers are widening their marketing management approach, including the use of social media to communicate with consumers and to promote healthy food. The purpose of this paper is to investigate European retailers’ social media communication content used to promote healthy food products, by analysing retailers’ Twitter messages and accounts characteristics, retailers’ Twitter messages content on healthy food and retailers’ Twitter accounts orientation on healthy food. Design/methodology/approach Data include approximately 74,000 tweets sent in 2016 from 90 corporate and brand accounts. The tweets were sent by the top 36 European retailers. Data elaboration includes quantitative content analysis of Twitter messages, which is used to identify healthy food categories’ occurrences and co-occurrences. Then, multiple multivariate-linear regression analyses explore the relation between retailers’ characteristics and healthy food messaging and between the overall content of retailer accounts and a healthy food focus. Findings The vast majority of retailers’ tweets on healthy food issues mainly address general health and sustainability issues. Tweets about food health and nutrition refer to food types, meals or consumer segments. Tweets about food sustainability refer to general issues. Analysis of retailer accounts shows that the larger the retailer is, the lower the relevance of healthy food. Retailers with high numbers of tweets and followers tend to decrease their attention to healthy food promotion. Compared to retailers with lower revenues, retailers with higher revenues tend to send a higher number of tweets that focus on healthy food but the incidence is lower compared to the overall accounts’ messaging. Research limitations/implications As the study focuses on a single category of food products, further research into other categories of retail products may contribute to a wider perspective. Future research may include graphical content/emoticons and extend the analysis to other social media platforms. Finally, social media data allow studies to cover a wide geographical area. However, in order to also value non-English written messaging, this research introduces some approximations in language interpretation. Practical implications The research provides insights into how retailers use social media and provides an overview of how retailers manage their social media communication in one of the most promising food product categories. Retailers manage social media communication content cautiously to minimise controversial issues. This study provides insights into the need to more effectively target the increasing number of social media users. Originality/value The research approach and findings of this study extend prior research on retailers’ communication management by improving the understanding of retailers’ use of social media and marketing communication content for their key products, focusing on healthy food.

Social media: communication strategies, engagement and future research directions

2017 ◽  
Vol 29 (1) ◽  
pp. 2-19 ◽  
Author(s):  
Rebecca Dolan ◽  
Jodie Conduit ◽  
John Fahy ◽  
Steve Goodman
Keyword(s):  
Social Media ◽  
Research Agenda ◽  
Communication Strategies ◽  
Customer Engagement ◽  
Future Research ◽  
Media Communication ◽  
Content Type ◽  
Future Research Agenda ◽  
Social Media Communication ◽  
The Impact

Purpose This study aims to use social media data to identify brand communication strategies on Facebook. The analysis uncovers trends and statistics regarding engagement rates. This research leads to the development of a future research agenda for social media and engagement research. Design/methodology/approach The Facebook Insights data of 12 wine brands over a 12-month period informed this study. Descriptive analysis was undertaken to examine the social media communication strategies of these brands. The impact of these strategies on engagement metrics is also examined. Findings The findings demonstrate a low rate of engagement among the users of the wine brand Facebook pages. A majority of Facebook fans rarely engage with the brands. The results demonstrate that user engagement varies depending on the day of the week and hour of the day of the brand post. Practical implications Wine brands can use these findings as a guideline for effective practice and as a benchmarking tool for assessing their social media performance. The paper provides implications for marketing scholars through the development of a future research agenda related to social media, customer engagement and wine marketing. Originality/value This paper fulfils an identified need by offering practical advice to wine producers on the necessity to explore and understand social media strategy and customer engagement characteristics.

scholarly journals Emoji Identification and Prediction in Hebrew Political Corpus

10.28945/4372 ◽  
2019 ◽  
Vol 16 ◽  
pp. 343-359
Author(s):  
Chaya Liebeskind
Keyword(s):  
Social Media ◽  
Text Message ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Media Communication ◽  
Prediction Task ◽  
Morphologically Rich Languages ◽  
Definition Of ◽  
Textual Content ◽  
Social Media Communication

Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning (ML) approach. We compare two text representation approaches, i.e., n-grams and character n-grams and analyze the contribution of additional metadata features to the classification. Contribution: The task of emoji identification is novel. We extend the definition of the emoji prediction task by allowing to use not only the textual content but also meta-data analysis. Findings: Metadata improve the classification accuracy in the task of emoji identification. In the task of emoji prediction it is better to apply feature selection. Recommendations for Practitioners: In many of the cases the classifier decision seems fitter to the comment content than the emoji that was chosen by the commentator. The classifier may be useful for emoji suggestion. Recommendation for Researchers: Explore character-based representations rather than word-based representations in the case of morphologically rich languages. Impact on Society: Improve the modeling of social media communication. Future Research: We plan to address the multi-label setting of the emoji prediction task and to investigate the deep learning approach for both of our classification tasks

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