scholarly journals Opinions that matter: the hybridization of opinion and reputation measurement in social media listening software

2020 ◽  
Vol 42 (7-8) ◽  
pp. 1495-1511 ◽  
Author(s):  
Baptiste Kotras
Keyword(s):  
Social Media ◽  
Reputation Management ◽  
Social Media Data ◽  
Reputation Measurement ◽  
Collective Process ◽  
Spontaneous Expression ◽  
Definition Of ◽  
Online Reputation ◽  
Start Ups ◽  
Media Data

Since the 2000s, numerous start-ups and agencies have argued for the necessity of analyzing social media data to ‘know what people think’, as they are deemed to provide access to spontaneous expression of thoughts, tastes, and representations. How do these actors, and the various types of knowledge and technology they draw upon, change the way we know and act upon people’s opinions? This article offers insight on these understudied actors, by describing the emergence in France of a market for measuring online opinion. It shows two distinct trajectories of innovation, and the key role played by the early clients of these companies and by the demand for tools for online reputation management in the shaping of these instruments, and the definition of epistemic value. Both approaches of online opinion break with the classical egalitarian conception of public opinion. They instead conceive opinion as a mediated and collective process in which not all opinions have an equal value.

scholarly journals Inferring Activities from Social Media Data

2017 ◽  
Vol 2666 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Emmanouil Chaniotakis ◽  
Constantinos Antoniou ◽  
Georgia Aifadopoulou ◽  
Loukas Dimitriou
Keyword(s):  
Social Media ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Social Media Data ◽  
Use Of Social Media ◽  
Definition Of ◽  
The Individual ◽  
Transportation Research ◽  
Direct Use ◽  
Media Data

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.

scholarly journals Blending Words Found In Social Media

2017 ◽  
Vol 4 (2) ◽  
pp. 65
Author(s):  
Giyatmi Giyatmi ◽  
Ratih Wijayava ◽  
Sihindun Arumi
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Research Data ◽  
Word Formation ◽  
Data Sources ◽  
Social Media Data ◽  
New Words ◽  
The Social ◽  
Definition Of ◽  
Media Data

There are many new words from the social media such as Netizen, Trentop, and Delcon. Those words include in blending. Blending is one of word formations combining two clipped words to form a brand new word. The researchers are interested in analyzing blend words used in the social media such as Instagram, Twitter, Facebook, and Blackberry Messenger. This research aims at (1) finding blend words used in the social media (2) describing kinds of blend words used in social media (3) describing the process of blend word formation used in the social media. This research uses some theories dealing with definition of blending and kinds of blending. This research belongs to descriptive qualitative research. Data of the research are English blend words used in social media. Data sources of this research are websites consisting of some English words used in social media and some social media users as the informant. Techniques of data collecting in this research are observation and simak catat. Observation is by observing some websites consisting of some English words used in social media. Simak catat is done by taking some notes on the data and encoding in symbols such as No/Blend words/Kinds of Blending. The researchers use source triangulation to check the data from the researchers with the informant and theory triangulation to determine kinds of blending and blend word formation in social media. There are115 data of blend words. Those data consists of 65 data of Instagram, 47 data of Twitter, 1 datum of Facebook, and 2 data of Blackberry Messenger. There are 2 types of blending used in social media;108 data of blending with clipping and 7 data of blending with overlapping. There are 10 ways of blend word formation found in this research.

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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