The digital traces of user-generated content: how social media data may become the historical sources of the future

2018 ◽  
pp. 61-86
Author(s):  
Katrin Weller
Keyword(s):  
Social Media ◽  
User Generated Content ◽  
Historical Sources ◽  
Social Media Data ◽  
The Future ◽  
Media Data

scholarly journals A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology

10.2196/25028 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e25028
Author(s):  
Ji-Hyun Lee ◽  
Hyeoun-Ae Park ◽  
Tae-Min Song
Keyword(s):  
Social Media ◽  
Ecological Model ◽  
Description Logics ◽  
Fertility Rate ◽  
Low Fertility ◽  
Ontology Development ◽  
Social Media Data ◽  
The Future ◽  
Content Coverage ◽  
Media Data

Background South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends. Objective The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues. Methods An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea’s Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals. Results A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future. Conclusions The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.

scholarly journals How Social Media Impacts Brand Value: The Mediating Role of Customer Satisfaction

2020 ◽  
Vol 13 (1) ◽  
pp. 82-96
Author(s):  
Anatoli Colicev ◽  
Pete O’Connor
Keyword(s):  
Social Media ◽  
Customer Satisfaction ◽  
User Generated Content ◽  
Social Media Marketing ◽  
Brand Value ◽  
Social Media Data ◽  
Mediating Role ◽  
Social Media Platforms ◽  
Media Data

The growing popularity of social media platforms has increased brand investments in social media marketing. However, it is not clear whether and how social media marketing leads to the creation of value for consumers and brands; therefore, we investigate how marketer and user-generated content on social media affects consumer and brand metrics. Based on the marketing productivity chain, we propose that customer satisfaction, a leading consumer metric, mediates the link between social media content and brand value. To test such assertions, we use a sample of 87 brands from 17 industries and collect a unique dataset that combines social media data from Facebook, Twitter, and YouTube with customer satisfaction, brand value, and advertising expenses. We find that user-generated content has a stronger effect on customer satisfaction than marketer-generated content. We also find that YouTube is the most effective platform for user generated content. Interestingly, we find that the effects of marketer-generated content depend on the brand’s corporate reputation. In other words, more reputable brands can leverage their marketer-generated content more effectively.

scholarly journals Sentimental Analysis and Detection of Rumours for Social Media Data using Logistic Regression

2019 ◽  
Vol 9 (1) ◽  
pp. 2123-2126
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
User Generated Content ◽  
Ancient Text ◽  
Social Media Data ◽  
Social Media Platform ◽  
Subjective Knowledge ◽  
Media Platform ◽  
Media Data

Over the last decade ,the Internet has become an ubiquitous and enormous suffuse medium of the user generated content and self-opinionated knowledge. Users currently have the facility to specify their views, opinions and ideas publically. Victimizing social media platform is a place where people can express their mindsets and feelings in a well associated manner and hence is productive and economical . These ever-growing subjective knowledge are doubtless, an especially made for supply of data of any reasonably method process. The Sentiment Analysis aims at distinctive self-opinionated knowledge during an Internet and classifying them in line with their polarity whether or not they contain positive ,negative or neutralizing references. Sentiment Analysis could be a drawback of text based mostly analysis however there are difficulties which are needed to be pondered upon that would create a tough parameter as compared to ancient text based analysis. It depicts the state where it has a desire of trial to figure out these issues and it's spread out many chances for further analysis for handling negative sentences, hidden emotions , slangs and sentence sarcasm. The project also proposes additional features compared to other previous model projects by enabling the detection of rumor , identifying and analyzing whether message given via user belongs to rumor category or not using Logistic Regression process in Machine Learning domain.

scholarly journals A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology (Preprint)

2020 ◽  
Author(s):  
Ji-Hyun Lee ◽  
Hyeoun-Ae Park ◽  
Tae-Min Song
Keyword(s):  
Social Media ◽  
Ecological Model ◽  
Description Logics ◽  
Fertility Rate ◽  
Low Fertility ◽  
Ontology Development ◽  
Social Media Data ◽  
The Future ◽  
Content Coverage ◽  
Media Data

BACKGROUND South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends. OBJECTIVE The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues. METHODS An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea’s Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals. RESULTS A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future. CONCLUSIONS The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.

scholarly journals Attitudes towards Brands and Advertisements: Qualitative and Thematic Analysis of Social Media Data

2019 ◽  
Author(s):  
Emmanuel Mogaji ◽  
Temitope Farinloye
Keyword(s):  
Social Media ◽  
Research Method ◽  
User Generated Content ◽  
Social Media Data ◽  
Perceptions And Attitudes ◽  
Public Data ◽  
Social Media Platforms ◽  
The Mind ◽  
Media Data ◽  
Insight Into

<div>Social media has been described as a platform for discussing ideas, communicating experiences and exchanging knowledge. It has changed the way individuals interact, providing massive amount of data and rich market insight as customers and brands engage and build relationships. This public declaration is of great concern for any organisation as it transfers the power to shape brand images from the hands of advertisers to the words of consumers’ online connections.</div><div>This chapter sets an agenda proposing the possibilities of qualitatively analysing user-generated content on social media platforms to provide insight into attitudes towards advertisements and their brands. Unlike participants being interviewed in a focus group, filling in questionnaires or neuroscience providing insight into how the mind perceives advertisements which typically requires expensive, bulky equipment and lab-type settings that limit and influence the experience, this is readily available public data which can be thematically analysed to add to existing knowledge.</div><div>Presenting the idea, publicly declared responses to the advertisements of UK banks on Facebook were analysed in order to gain insight into their perceptions and attitudes towards the advertisements and their brands. An outline of how to perform an analysis of user-generated content was provided to buttress the research method. Challenges and limitations of this research method were also considered.</div>

Devising Parametric User Models for Processing and Analysing Social Media Data to Influence User Behaviour

2017 ◽  
pp. 1-41 ◽  
Author(s):  
Jonathan Bishop
Keyword(s):  
Social Media ◽  
User Generated Content ◽  
User Models ◽  
Social Media Data ◽  
User Behaviour ◽  
Media Data

Academia is often plagued with those who define themselves by whether they are “quantitative” or “qualitative.” This chapter contests that when it comes to researching social media the two are inseparable in datafying user generated content. Posts on Twitter for instance have a textual element to the narratives that could be considered qualitative, but also quantitative criteria can be applied. Interviewing approaches can allow for the exploration of discourses to produce new theories, which may then rely of those approaches commonly thought of as quantitative. This chapter tests out a variety of different approaches to show how it is only through using all approaches available can social media be triangulated to produce accurate modelling of user behaviour.

scholarly journals Attitudes towards Brands and Advertisements: Qualitative and Thematic Analysis of Social Media Data

2019 ◽  
Author(s):  
Emmanuel Mogaji ◽  
Temitope Farinloye
Keyword(s):  
Social Media ◽  
Research Method ◽  
User Generated Content ◽  
Social Media Data ◽  
Perceptions And Attitudes ◽  
Public Data ◽  
Social Media Platforms ◽  
The Mind ◽  
Media Data ◽  
Insight Into

<div>Social media has been described as a platform for discussing ideas, communicating experiences and exchanging knowledge. It has changed the way individuals interact, providing massive amount of data and rich market insight as customers and brands engage and build relationships. This public declaration is of great concern for any organisation as it transfers the power to shape brand images from the hands of advertisers to the words of consumers’ online connections.</div><div>This chapter sets an agenda proposing the possibilities of qualitatively analysing user-generated content on social media platforms to provide insight into attitudes towards advertisements and their brands. Unlike participants being interviewed in a focus group, filling in questionnaires or neuroscience providing insight into how the mind perceives advertisements which typically requires expensive, bulky equipment and lab-type settings that limit and influence the experience, this is readily available public data which can be thematically analysed to add to existing knowledge.</div><div>Presenting the idea, publicly declared responses to the advertisements of UK banks on Facebook were analysed in order to gain insight into their perceptions and attitudes towards the advertisements and their brands. An outline of how to perform an analysis of user-generated content was provided to buttress the research method. Challenges and limitations of this research method were also considered.</div>

scholarly journals Correction: A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology (Preprint)

2021 ◽  
Author(s):  
Ji-Hyun Lee ◽  
Hyeoun-Ae Park ◽  
Tae-Min Song
Keyword(s):  
Social Media ◽  
Ecological Model ◽  
Description Logics ◽  
Fertility Rate ◽  
Low Fertility ◽  
Ontology Development ◽  
Social Media Data ◽  
The Future ◽  
Content Coverage ◽  
Media Data

BACKGROUND South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends. OBJECTIVE The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues. METHODS An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea’s Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals. RESULTS A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future. CONCLUSIONS The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.

scholarly journals Mapping the languages of Twitter in Finland:

2020 ◽  
Vol 121 (1) ◽  
pp. 12-44
Author(s):  
Tuomo Hiippala ◽  
Tuomas Väisänen ◽  
Tuuli Toivonen ◽  
Olle Järv
Keyword(s):  
Social Media ◽  
Urban Areas ◽  
High Volume ◽  
User Generated Content ◽  
Social Media Data ◽  
Fine Grained ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
Media Platform ◽  
Media Data

Twitter is a popular social media platform for scholarly research, because the user-generated content on the platform can also include geographic and temporal information. We collect a corpus of 38 million Twitter messages with two million geographical coordinates to map the languages used across Finland at the level of regions and municipalities. To cope with the high volume of social media data, we use automatic language identification and place of residence detection. We estimate the linguistic richness and diversity of users and locations using measures developed within ecology and information sciences. The analyses reveal a rich, multilingual environment that varies geographically and temporally, particularly between coastal, rural and urban areas. The results, which underline the mutual benefits of collaboration between linguists and geographers, provide a more fine-grained, accurate and comprehensive view of the languages used on Twitter in Finland than previously available.

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