Final Report of the NTCIR-14 FinNum Task: Challenges and Current Status of Fine-Grained Numeral Understanding in Financial Social Media Data

2019 ◽  
pp. 183-192 ◽  
Author(s):  
Chung-Chi Chen ◽  
Hen-Hsen Huang ◽  
Hiroya Takamura ◽  
Hsin-Hsi Chen
Keyword(s):  
Social Media ◽  
Current Status ◽  
Final Report ◽  
Social Media Data ◽  
Fine Grained ◽  
Media Data

scholarly journals Hybrid Approaches to Fine-Grained Emotion Detection in Social Media Data

2020 ◽  
Vol 34 (10) ◽  
pp. 13732-13733
Author(s):  
Annika Marie Schoene
Keyword(s):  
Social Media ◽  
Recurrent Neural Networks ◽  
Emotion Detection ◽  
Problem Statement ◽  
Future Directions ◽  
Social Media Data ◽  
Fine Grained ◽  
Hybrid Approaches ◽  
Research Plan ◽  
Media Data

This paper states the challenges in fine-grained target-dependent Sentiment Analysis for social media data using recurrent neural networks. First, the problem statement is outlined and an overview of related work in the area is given. Then a summary of progress and results achieved to date and a research plan and future directions of this work are given.

scholarly journals Fine-Grained Subjective Partitioning of Urban Space Using Human Interactions From Social Media Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 52085-52094 ◽  
Author(s):  
Mengling Qiao ◽  
Yandong Wang ◽  
Shanmei Wu ◽  
An Luo ◽  
Shisi Ruan ◽  
...  
Keyword(s):  
Social Media ◽  
Urban Space ◽  
Social Media Data ◽  
Fine Grained ◽  
Human Interactions ◽  
Media Data

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.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

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