scholarly journals Reports of the Workshops Held at the 2018 International AAAI Conference on Web and Social Media

AI Magazine ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 36-44
Author(s):  
Managing Editor ◽  
Jisun An ◽  
Rumi Chunara ◽  
David J. Crandall ◽  
Darian Frajberg ◽  
...  
Keyword(s):  
Social Media ◽  
Well Being ◽  
Ecological Monitoring ◽  
Online Data ◽  
Social Media Data ◽  
Ethical Trade ◽  
Making Sense ◽  
Trade Offs ◽  
Informal Setting ◽  
Media Data

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s 12th International Conference on Web and Social Media (AAAI-18) was held at Stanford University, Stanford, California USA, on Monday, June 25, 2018. There were fourteen workshops in the program: Algorithmic Personalization and News: Risks and Opportunities; Beyond Online Data: Tackling Challenging Social Science Questions; Bridging the Gaps: Social Media, Use and Well-Being; Chatbot; Data-Driven Personas and Human-Driven Analytics: Automating Customer Insights in the Era of Social Media;  Designed Data for Bridging the Lab and the Field: Tools, Methods, and Challenges in Social Media Experiments; Emoji Understanding and Applications in Social Media; Event Analytics Using Social Media Data; Exploring Ethical Trade-Offs in Social Media Research; Making Sense of Online Data for Population Research; News and Public Opinion; Social Media and Health: A Focus on Methods for Linking Online and Offline Data; Social Web for Environmental and Ecological Monitoring and The ICWSM Science Slam. Workshops were held on the first day of the conference. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Organizers from nine of the  workshops submitted reports, which are reproduced in this report. Brief summaries of the other five workshops have been reproduced from their website descriptions.

scholarly journals Tackling social media data analysis

2019 ◽  
Vol 22 (2) ◽  
pp. 94-113 ◽  
Author(s):  
Violetta Wilk ◽  
Geoffrey N. Soutar ◽  
Paul Harrigan
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Exploratory Research ◽  
Online Data ◽  
Social Media Data ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Brand Advocacy ◽  
Qualitative Data Analysis Software ◽  
Media Data

PurposeThis paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.Design/methodology/approachThis study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.FindingsThis research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.Research limitations/implicationsQualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.Practical implicationsThe paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.Originality/valueThis paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

scholarly journals Bi-Lingual (English, Punjabi) Sarcastic Sentiment Analysis by using Classification Methods

2019 ◽  
Vol 8 (9) ◽  
pp. 1383-1388
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Step ◽  
Online Data ◽  
Social Media Data ◽  
The Social ◽  
Negative Comments ◽  
Regional Languages ◽  
Media Data ◽  
Day By Day

Sentiment analysis is one of the heated topic in the field of text mining. As the social media data is increased day by day the main need of the data scientists is to classify the data so that it can be further used for decision making or knowledge discovery. Now –a-days everything and everyone available online so to check the latest trends in business or in daily life one must consider the online data. The main focus of sentiment analysis is to focus on positive or negative comments so that a well define picture is created that what is trending or not but the sarcasm manipulates the data as in sarcastic comment negative comment consider as positive because of the presence of positive words in the comment or data so it is necessary to detect the sarcasm in online data . The data on social media is available in various languages so sentiment analysis in regional languages is also a main step . In the proposed work we focus on two languages i.e Punjabi and English. Here we use deep learning based neural networks for the sarcasm detection in English as well as Punjabi language. In the proposed work we consider three datasets i.e. balanced English dataset, Balanced Punjabi Dataset and unbalanced Punjabi dataset. We used six different models to check the accuracy of the classified data the models we used are LSTM with word embedding layer, BiLSTM with , LSTM+LSTM, BiLSTM+BiLSTM, LSTM+BiLSTM, CNN respectively. LSTM provide better accuracy for balanced Punjabi and English dataset i.e. 95.63% and 94.17% respectively. The accuracy for unbalanced Punjabi dataset is provided by BiLSTM i.e.96.31%.

scholarly journals From FAIR data to fair data use: Methodological data fairness in health-related social media research

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110103
Author(s):  
Sabina Leonelli ◽  
Rebecca Lovell ◽  
Benedict W Wheeler ◽  
Lora Fleming ◽  
Hywel Williams
Keyword(s):  
Social Media ◽  
Well Being ◽  
Research Process ◽  
Future Research ◽  
Social Media Data ◽  
Data Practices ◽  
Health Related Research ◽  
Health Related ◽  
Use Of Social Media ◽  
Media Data

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced.

scholarly journals Reports of the Workshops Held at the 2017 International AAAI Conference on Web and Social Media

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 93-98
Author(s):  
Jisun An ◽  
Giovanni Luca Ciampaglia ◽  
Nir Grinberg ◽  
Kenneth Joseph ◽  
Alexios Mantzarlis ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Observational Studies ◽  
The Other ◽  
Current Interest ◽  
Social Media Data ◽  
Demographic Research ◽  
User Perceptions ◽  
Informal Setting ◽  
Media Data

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s International Conference on Web and Social Media (AAAI-17) was held in Montréal, Québec, Canada on Tuesday, May 15, 2017. There were eight workshops in the program: Digital Misinformation, Events Analytics Using Social Media Data, News and Public Opinion, Observational Studies through Social Media, Perceptual Biases and Social Media, Social Media and Demographic Research, Studying User Perceptions and Experiences with Algorithms, The ICWSM Science Slam. Workshops were held on the first day of the conference. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Organizers from two of the workshop chose to include papers in the AAAI Technical Reports series (Observational Studies through Social Media and News and Public Opinion). Their papers were included as a nonarchival part of the ICWSM proceedings. Organizers from four of the workshops (Digital Misinformation, News and Public Opinion, Perceptual Biases and Social Media, and Studying User Perceptions and Experiences with Algorithms) submitted reports, which are reproduced in this report. Brief summaries of the other four workshops have been reproduced from their website descriptions.

scholarly journals Understanding Consumer Preferences from Social Media Data

2019 ◽  
Vol 11 (2) ◽  
pp. 48-53 ◽  
Author(s):  
Bradley Taylor
Keyword(s):  
Social Media ◽  
Consumer Preferences ◽  
Product Reviews ◽  
Online Data ◽  
Social Media Data ◽  
Processing Power ◽  
Textual Data ◽  
The Common ◽  
Media Data ◽  
Answering Questions

AbstractConsumers produce enormous amounts of textual data of product reviews online. Artificial intelligence (AI) can help analyze this data and generate insights about consumer preferences and decision-making. A GfK research project tested how we can use AI to learn consumer preferences and predict choices from publicly available social media and review data. The common AI tool “Word Embeddings” was used and has shown to be a powerful way to analyze the words people use. It helped reveal consumers’ preferred brands, favorite features and main benefits. Language biases uncovered by the analysis can indicate preferences. Compared to actual sales data from GfK panels, they fit reasonably within various categories. Especially when data volumes were large, the method produced very accurate results. By using free, widespread online data it is completely passive, without affecting respondents or leading them into ranking or answering questions they would otherwise not even have thought of. The analysis is fast to run and no fancy processing power is needed.

scholarly journals Emergent social cohesion for coping with community disruptions in disasters

2020 ◽  
Vol 17 (164) ◽  
pp. 20190778 ◽  
Author(s):  
Chao Fan ◽  
Yucheng Jiang ◽  
Ali Mostafavi
Keyword(s):  
Social Media ◽  
Social Cohesion ◽  
Social Ties ◽  
Well Being ◽  
Social Media Data ◽  
Emergency Managers ◽  
Communication History ◽  
And Performance ◽  
Underlying Mechanisms ◽  
Media Data

Social cohesion is an important determinant of community well-being, especially in times of distress such as disasters. This study investigates the phenomena of emergent social cohesion, which is characterized by abrupt, temporary and extensive social ties with the goal of sharing and receiving information regarding a particular event influencing a community. In the context of disasters, emergent social cohesion, enabled by social media usage, could play a significant role in improving the ability of communities to cope with disruptions in recent disasters. In this study, we employed a network reticulation framework to examine the underlying mechanisms influencing emergent social cohesion on social media while communities cope with disaster-induced disruptions. We analysed neighbourhood-tagged social media data (social media data whose users are tagged by neighbourhoods) in Houston, TX, USA, during Hurricane Harvey to characterize four modalities of network reticulation (i.e. enactment, activation, reticulation and performance) giving rise to emergent social cohesion. Our results show that, unlike regular social cohesion, communication history and physical proximity do not significantly affect emergent social cohesion. The results also indicate that weak social ties play an important role in bridging different social network communities, and hence reinforce emergent social cohesion. The findings can inform public officials, emergency managers and decision-makers regarding the important role of neighbourhood-tagged social media, as a new form of community infrastructure, for improving the ability of communities to cope with disaster disruptions through enhanced emergent social cohesion.

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