scholarly journals Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

2019 ◽  
Vol 33 ◽  
pp. 232-240 ◽  
Author(s):  
Jinfeng Rao ◽  
Wei Yang ◽  
Yuhao Zhang ◽  
Ferhan Ture ◽  
Jimmy Lin
Keyword(s):  
Social Media ◽  
Ad Hoc ◽  
Similarity Measurement ◽  
Web Pages ◽  
Ranking Models ◽  
Twitter Data ◽  
The Social ◽  
Ad Hoc Retrieval ◽  
Feature Based ◽  
Substantial Interest

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A poolingbased similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011–2014 show that our model significantly outperforms prior feature-based as well as existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. Our code and data are publicly available.1

scholarly journals FEATURE BASED ANALYSIS ON THE DUPLICATE FAKE ACCOUNT ON TWITTER USING INTELLIGENT MECHANISM

2021 ◽  
Vol 9 (1) ◽  
pp. 1315-1320
Author(s):  
Dr. Mohammed Ali Alhariri
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
Social Media Platform ◽  
Twitter Data ◽  
Learning Techniques ◽  
The Social ◽  
Feature Based ◽  
Media Platform

The duplicate fake accounts are detected in this work the data from the social media platform is accessed. The platform choose to use the analysis on social media platform is selected as twitter. The twitter data is accessed using Twitter API, with using some selected features that remain the most appropriate regarding the reason of duplicate fake account. The feature based analysis is compared using machine learning techniques, Random Forest, Decision Tree, and SVM. The performance is further analyzed based on accuracy SVM performed 93.3% accuracy, where decision tree performed as 89.0% and random forest performed as 85.5%. The better performance observed using feature-based analysis is of SVM.  

scholarly journals Social Media Engagement at Academic Conferences: Report of the Association of Pathology Chairs 2018 and 2019 Annual Meeting Social Media Committee

2020 ◽  
Vol 7 ◽  
pp. 237428952093401
Author(s):  
Yonah C. Ziemba ◽  
Dana Razzano ◽  
Timothy C. Allen ◽  
Adam L. Booth ◽  
Scott R. Anderson ◽  
...  
Keyword(s):  
Social Media ◽  
Ad Hoc ◽  
Fold Increase ◽  
Outcome Data ◽  
Medical Specialties ◽  
The Social ◽  
Social Media Engagement ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Academic Conferences

The use of social media at academic conferences is expanding, and platforms such as Twitter are used to share meeting content with the world. Pathology conferences are no exception, and recently, pathology organizations have promoted social media as a way to enhance meeting exposure. A social media committee was formed ad hoc to implement strategies to enhance social media involvement and coverage at the 2018 and 2019 annual meetings of the Association of Pathology Chairs. This organized approach resulted in an 11-fold increase in social media engagement compared to the year prior to committee formation (2017). In this article, the social media committee reviews the strategies that were employed and the resultant outcome data. In addition, we categorize tweets by topic to identify the topics of greatest interest to meeting participants, and we discuss the differences between Twitter and other social media platforms. Lastly, we review the existing literature on this topic from 23 medical specialties and health care fields.

scholarly journals The use made of video in the social media by candidates in the 2019 Valencian Autonomous Government elections

2020 ◽  
Vol 5 ◽  
pp. 111-124
Author(s):  
Carlos López Olano ◽  
Sebastián Sánchez Castillo ◽  
Benjamín Marín Pérez
Keyword(s):  
Social Media ◽  
Ad Hoc ◽  
Communication Strategy ◽  
Traditional Media ◽  
Video Information ◽  
The Social ◽  
Wide Range ◽  
Political Choices ◽  
Left And Right ◽  
Political Spectrum

Videos are increasingly being used in social networks for a wide range of purposes, including political campaigning. Here, social media seem to be gaining an edge over the mainstream variety when it comes to making political choices, especially during election campaigns. This paper examines the extent to which social media is used in Valencian Autonomous Government elections and looks at each of the candidate's experiences in this regard in the April 2019 elections. We pay particular attention to the differences between the three networks analysed — Facebook, Twitter and Instagram, and consider what kind of video information is shared. For these purposes, we create nine formal categories, some of which draw on traditional media while others are created ad hoc for our study. Based on these categories, we identify which media are most used, and give guidelines on best practices. We also consider differences in usage between politicians from the left and right ends of the political spectrum. The results point to a general lack of communication strategy in candidates’ use of discretionary video materials.

scholarly journals Sentiment Analysis Of Twitter Data By Using Deep Learning And Machine Learning

2021 ◽  
Vol 12 (6) ◽  
pp. 962-970
Author(s):  
Prof. Manisha Sachin Dabade, Et. al.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Daily Monitoring ◽  
Fine Grain ◽  
Large Size ◽  
Twitter Data ◽  
The Social ◽  
Enormous Amount

In today’s world, social media is viral and easily accessible. The Social media sites like Twitter, Facebook, Tumblr, etc. are a primary and valuable source of information.Twitter is a micro-blogging platform, and it provides an enormous amount of data. Such type of information can use for different sentiment analysis applications such as reviews, predictions, elections, marketing, etc. It is one of the most popular sites where peoples write tweets, retweets, and interact daily. Monitoring and analyzing these tweets give valuable feedback to users. Due to this data's large size, sentiment analysis is using to analyze this data without going through millions of tweets manually. Any user writes their reviews about different products, topics, or events on Twitter, called tweets and retweets. People also use emojis such as happy, sad, and neutral in expressing their emotions, so these sites contain expansive volumes of unprocessed data called raw data. The main goal of this research is to recognize the algorithms by using Machine Learning Classifiers. The study intends to categorize Fine-grain sentiments within Tweets of Vaccination (89974 tweets) through machine learning and a deep learning approach. The study takes consideration of both labeled and unlabeled data. It also detects emojis from tweets using machine learning libraries like Textblob, Vadar, Fast text, Flair, Genism, spaCy, and NLTK.

scholarly journals SPATIAL-TEMPORAL ANALYSIS OF SOCIAL MEDIA DATA RELATED TO NEPAL EARTHQUAKE 2015

2016 ◽  
Vol XLI-B2 ◽  
pp. 567-571
Author(s):  
L. Thapa
Keyword(s):  
Social Media ◽  
Temporal Analysis ◽  
Archaeological Sites ◽  
Social Media Data ◽  
User Input ◽  
Nepal Earthquake ◽  
Twitter Data ◽  
The Social ◽  
Media Data ◽  
Spatial Temporal Analysis

Social Medias these days have become the instant communication platform to share anything; from personal feelings to the matter of public concern, these are the easiest and aphoristic way to deliver information among the mass. With the development of Web 2.0 technologies, more and more emphasis has been given to user input in the web; the concept of Geoweb is being visualized and in the recent years, social media like Twitter, Flicker are among the popular Location Based Social Medias with locational functionality enabled in them. Nepal faced devastating earthquake on 25 April, 2015 resulting in the loss of thousands of lives, destruction in the historical-archaeological sites and properties. Instant help was offered by many countries around the globe and even lots of NGOs, INGOs and people started the rescue operations immediately; concerned authorities and people used different communication medium like Frequency Modulation Stations, Television, and Social Medias over the World Wide Web to gather information associated with the Quake and to ease the rescue activities. They also initiated campaign in the Social Media to raise the funds and support the victims. Even the social medias like Facebook, Twitter, themselves announced the helping campaign to rebuild Nepal. In such scenario, this paper features the analysis of Twitter data containing hashtag related to Nepal Earthquake 2015 together with their temporal characteristics, when were the message generated, where were these from and how these spread spatially over the internet?

The Dynamics of Content Popularity in Social Media

2011 ◽  
pp. 17-33
Author(s):  
Symeon Papadopoulos ◽  
Athena Vakali ◽  
Ioannis Kompatsiaris
Keyword(s):  
Social Media ◽  
Social Relations ◽  
Online Social Networks ◽  
Web Pages ◽  
Social Bookmarking ◽  
Consumption Behavior ◽  
Content Consumption ◽  
The Social ◽  
Content Popularity ◽  
The Impact

Social Bookmarking Systems (SBS) have been widely adopted in the last years, and thus they have had a significant impact on the way that online content is accessed, read and rated. Until recently, the decision on what content to display in a publisher’s web pages was made by one or at most few authorities. In contrast, modern SBS-based applications permit their users to submit their preferred content, to comment on and to rate the content of other users and establish social relations with each other. In that way, the vision of the social media is realized, i.e. the online users collectively decide upon the interestingness of the available bookmarked content. This paper attempts to provide insights into the dynamics emerging from the process of content rating by the user community. To this end, the paper proposes a framework for the study of the statistical properties of an SBS, the evolution of bookmarked content popularity and user activity in time, as well as the impact of online social networks on the content consumption behavior of individuals. The proposed analysis framework is applied to a large dataset collected from digg, a popular social media application.

Computing Opinions for Twitter Review Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4360-4363
Author(s):  
S. Tenkale Pallavi ◽  
S. Jagannatha
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Web Pages ◽  
Human Being ◽  
Association Network ◽  
The Social ◽  
Individual Human

Customers and users post their opinions or reviews on social networking sites and it has increased the amount of data WWW. With this users from all over world try to share their opinions and sentiments on the blogging sites every day. Internet is being used in form of web pages, social media, and sometimes blogs which increases online portals sentiments, reviews, opinions, references, scores, and feedbacks are also generated by people. Twitter is the most famous micro-blogging site where users express their opinions in the form of tweets. The user can express their sentiments about various aspects e.g., books, celebrities, restaurants, various products, research, events, etc. All these opinions plays vital roles and they are quite important for various businesses, for government schemes, and for individual human being as well. Still, there are many curbs in mining reviews or opinions and process to calculate them. These limitations have turned into highland in investigating the actual gist of opinions and measuring its polarity. Hence, we recommend an inventive way to compute the sentiments for given reviews or opinions. This recommendation is centered on the social networking sites’ information of various Tweets, a word-emotion-association-network is put up in association to represent opinions and semantics that decides the base for the emotions (sentiment) analysis of opinion or reviews.

Revisiting Software Engineering in the Social Era

2016 ◽  
Vol 6 (4) ◽  
pp. 36-46 ◽  
Author(s):  
Vanilson Burégio ◽  
Ejub Kajan ◽  
Mohamed Sellami ◽  
Noura Faci ◽  
Zakaria Maamar ◽  
...  
Keyword(s):  
Social Media ◽  
Software Engineering ◽  
Ad Hoc ◽  
Social Systems ◽  
The Social ◽  
The Web

This paper discusses the possible changes that software engineering will have to go through in response to the challenges and issues associated with social media. Indeed, people have never been so connected like nowadays by forming spontaneous relations with others (even strangers) and engaging in ad-hoc interactions. The Web is the backbone of this new social era – an open, global, ubiquitous, and pervasive platform for today's society and world - suggesting that “everything” can socialize or be socialized. This paper also analyzes the evolution of software engineering as a discipline, points out the characteristics of social systems, and finally presents how these characteristics could affect software engineering's models and practices. It is expected that social systems' characteristics will make software engineering evolve one more time to tackle and address the social era's challenges and issues, respectively.

scholarly journals Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title

2021 ◽  
Vol 2070 (1) ◽  
pp. 012079
Author(s):  
V Jagadishwari ◽  
A Indulekha ◽  
Kiran Raghu ◽  
P Harshini
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Data Sets ◽  
Learning Models ◽  
Twitter Data ◽  
The Social ◽  
Social Media Text ◽  
Machine Learning Models

Abstract Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.

Can Firm Performance and Corporate Reputation Be Improved by Communicating CSR in Social Media?

2021 ◽  
Vol 8 (2) ◽  
pp. 1-20
Author(s):  
Julian Schröter ◽  
Andreas Dutzi ◽  
Eshari Withanage
Keyword(s):  
Social Media ◽  
Firm Performance ◽  
Significant Relationship ◽  
Corporate Reputation ◽  
Sustainability Index ◽  
Csr Communication ◽  
Twitter Data ◽  
The Social

As stakeholders make their decisions based on corporate reputation, it is vital for the companies to ensure that their CSR activities are communicated effectively via social media (SM) channels. It can be argued that by leveraging CSR in SM channels, firms have the possibility in strengthening trust and loyalty of their stakeholders and thereby enhancing corporate reputation and firm performances. Hence, the study aims to examine how CSR communication has an impact on firm performances and reputation. Top 50 and bottom 50 companies that are ranked in the Social Media Sustainability Index (2016) are collected along with four reputation ranking indices and Twitter data for this study. Although there is no significant relationship between Twitter and corporate reputation, there is a significant relationship between Twitter and firm performances.

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