scholarly journals #GoingtotheFair: a social media listening analysis of agricultural fairs1

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Julie A Mahoney ◽  
Nicole J O Widmar ◽  
Courtney L Bir
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Disease Risk ◽  
Swine Flu ◽  
Agricultural Product ◽  
Online Media ◽  
Specific Reference ◽  
Research Attention ◽  
Search Results ◽  
Agricultural Fairs

Abstract Agricultural fairs provide one of the last frontiers, and largest stages, for showcasing livestock agriculture to the public. However, public funding, attendance revenue, animal biosecurity, and public health concerns are all aspects worthy of conversation and increased research attention given the interaction between livestock animals and the general public in fair and festival settings. A prominent social media listening and data analytics platform was used to quantify online and social media chatter concerning agricultural fairs during a 27-mo period. A general search for online media referencing agricultural fair keywords was designed; social and online media mentions of agricultural fairs (n = 2,091,350 mentions) were further queried according to their reference to livestock, fair food, or the major agricultural product producing species of dairy and beef cattle (n = 68,900), poultry (n = 39,600), and swine (n = 31,250). Numbers of search results were found to be seasonal and Twitter was the single largest domain for all fair-related results; in contrast, the majority of livestock-related media was generated by news sources rather than from Twitter. On a weekly basis, the percentage of fair livestock mentions with species-specific reference was highly variable ranging from 0% to 86.8% for cattle, 0% to 85.7% for poultry, and 0% to 76.9% for swine. In addition to quantifying total search hits or mentions, the positivity/negativity of the search results was analyzed using natural language processing capabilities. The net sentiment quantified is the total percentage of positive posts minus the percentage of negative posts, which results in a necessarily bounded net sentiment between −100% and +100%. Overall net sentiment associated with mentions of agricultural fairs was positive; the topics garnering the highest positive sentiments were fair food and cattle (both 98% positive). Online discussion pertaining to agricultural fairs and swine was overall positive despite references to swine flu outbreaks. In conclusion, livestock and animal products had positive net sentiment over the time period studied, but there are multiple aspects of agricultural fairs worthy of further investigation and continued vigilance, including zoonotic disease risk and public perceptions of livestock industries.

scholarly journals Public Perceptions of Veterinarians from Social and Online Media Listening

2020 ◽  
Vol 7 (2) ◽  
pp. 75
Author(s):  
Nicole Widmar ◽  
Courtney Bir ◽  
John Lai ◽  
Christopher Wolf
Keyword(s):  
Social Media ◽  
Veterinary Medicine ◽  
Compassion Fatigue ◽  
Public Perception ◽  
Future Research ◽  
Online Media ◽  
Health Food ◽  
Search Results ◽  
Time Period ◽  
Media Sentiment

The public perception of the veterinary medicine profession is of increasing concern given the mounting challenges facing the industry, ranging from student debt loads to mental health implications arising from compassion fatigue, euthanasia, and other challenging aspects of the profession. This analysis employs social media listening and analysis to discern top themes arising from social and online media posts referencing veterinarians. Social media sentiment analysis is also employed to aid in quantifying the search results, in terms of whether they are positivity/negativity associated. From September 2017-November 2019, over 1.4 million posts and 1.7 million mentions were analyzed; the top domain in the search results was Twitter (74%). The mean net sentiment associated with the search conducted over the time period studied was 52%. The top terms revealed in the searches conducted revolved mainly around care of or concern for pet animals. The recognition of challenges facing the veterinary medicine profession were notably absent, except for the mention of suicide risks. While undeniably influenced by the search terms selected, which were directed towards client–clinic related verbiage, a relative lack of knowledge regarding veterinarians’ roles in human health, food safety/security, and society generally outside of companion animal care was recognized. Future research aimed at determining the value of veterinarians’ contributions to society and, in particular, in the scope of One Health, may aid in forming future communication and education campaigns.

scholarly journals Review On: Detection of Spam Comments Using NLP Algorithm

2018 ◽  
Vol 7 (01) ◽  
pp. 23386-23489
Author(s):  
Miss Rohini D.Warkar ◽  
Mr.I.R. Shaikh
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Social Networking ◽  
Real Time ◽  
Social Services ◽  
Language Processing ◽  
Social Networking Sites ◽  
Online Media

Detecting trending topics is perfect to summarize information getting from social media. To extract what topic is becoming hot on online media is one of the challenges. As we considering social media so social services are opportunity for spamming which greatly affect on value of real time search. Therefore the next task is to control spamming from social networking sites. For completing these challenges different concepts of data mining will be used. For now whatever work has been done is narrated below like spam control using natural language processing for preprocessing and clustering. One account has been created for making it real.

The effectiveness of Social Media Instagram as a Promotion of Agricultural product of fruits. (case :Instagram TaniHub Indonesia, Pasar Minggu, Jakarta Selatan)

2020 ◽  
Vol 4 (6) ◽  
pp. 866
Author(s):  
Nensy Yohana Natalia Pasaribu
Keyword(s):  
Social Media ◽  
Media Exposure ◽  
Agricultural Product ◽  
Marketing Activity ◽  
Feedback Message ◽  
The Social ◽  
The Media

Agriculture produces processed product which is perishable, so that the agricultural product should be distributed immediately. Processed product can be promoted to attract consumers to buy the product. One of the media that can be used to promote processed agricultural product is social media. Social media is needed to ease the marketing activity on the product. Social media is viral and can be delivered directly and personally to the consumer. Indicators are used to know the effectiveness of the social media as promotion media with AIDA concept. The results showed that promotion through Instagram has not been effective in the stages of attention (attention), interest (interest), desire (desire), and action (action). This study also explains that there is a relationship between the characteristics of gender followers and the level of social media exposure to the frequency of messages. In addition, there is also a relationship between the frequency of message feedback, message attractiveness, and intelligence in delivering messages with the interest stage. 

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

scholarly journals Evaluating Cultural Impact in Discursive Space through Digital Footprints

2021 ◽  
Vol 13 (7) ◽  
pp. 4043 ◽  
Author(s):  
Jesús López Baeza ◽  
Jens Bley ◽  
Kay Hartkopf ◽  
Martin Niggemann ◽  
James Arias ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Social Activity ◽  
Test Site ◽  
Cultural Discourse ◽  
Discursive Space ◽  
Topic Identification ◽  
Small Test ◽  
The Impact ◽  
The City

The research presented in this paper describes an evaluation of the impact of spatial interventions in public spaces, measured by social media data. This contribution aims at observing the way a spatial intervention in an urban location can affect what people talk about on social media. The test site for our research is Domplatz in the center of Hamburg, Germany. In recent years, several actions have taken place there, intending to attract social activity and spotlight the square as a landmark of cultural discourse in the city of Hamburg. To evaluate the impact of this strategy, textual data from the social networks Twitter and Instagram (i.e., tweets and image captions) are collected and analyzed using Natural Language Processing intelligence. These analyses identify and track the cultural topic or “people talking about culture” in the city of Hamburg. We observe the evolution of the cultural topic, and its potential correspondence in levels of activity, with certain intervention actions carried out in Domplatz. Two analytic methods of topic clustering and tracking are tested. The results show a successful topic identification and tracking with both methods, the second one being more accurate. This means that it is possible to isolate and observe the evolution of the city’s cultural discourse using NLP. However, it is shown that the effects of spatial interventions in our small test square have a limited local scale, rather than a city-wide relevance.

scholarly journals A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter

2021 ◽  
Author(s):  
Irina Wedel ◽  
Michael Palk ◽  
Stefan Voß
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Modeling ◽  
New Product ◽  
Business Value ◽  
Data Driven ◽  
New Product Introduction ◽  
Social Media Analytics ◽  
Product Introduction ◽  
Textual Data

AbstractSocial media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.

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