scholarly journals EmoMix+: An Approach of Depression Detection Based on Emotion Lexicon for Mobile Application

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Ran Li ◽  
Yuanfei Zhang ◽  
Lihua Yin ◽  
Zhe Sun ◽  
Zheng Lin ◽  
...  
Keyword(s):  
Social Media ◽  
Mobile Application ◽  
Negative Emotions ◽  
Experimental Results ◽  
Emotion Classification ◽  
Emotion Analysis ◽  
Fine Grained ◽  
Depression Detection ◽  
Emotion Lexicon

Emotion lexicon is an important auxiliary resource for text emotion analysis. Previous works mainly focused on positive and negative classification and less on fine-grained emotion classification. Researchers use lexicon-based methods to find that patients with depression express more negative emotions on social media. Emotional characteristics are an effective feature in detecting depression, but the traditional emotion lexicon has limitations in detecting depression and ignores many depression words. Therefore, we build an emotion lexicon for depression to further study the differences between healthy users and patients with depression. The experimental results show that the depression lexicon constructed in this paper is effective and has a better effect of classifying users with depression.

scholarly journals Exploring halal tourism tweets on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ali Feizollah ◽  
Mohamed M. Mostafa ◽  
Ainin Sulaiman ◽  
Zalina Zakaria ◽  
Ahmad Firdaus
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Topic Modeling ◽  
Semantic Network ◽  
Word List ◽  
Global Market ◽  
Semantic Network Analysis ◽  
Tourist Destinations ◽  
Muslim Countries ◽  
Emotion Lexicon

AbstractThis study explores tweets from Oct 2008 to Oct 2018 related to halal tourism. The tweets were extracted from twitter and underwent various cleaning processes. A total of 33,880 tweets were used for analysis. Analysis intended to (1) identify the topics users tweet about regarding halal tourism, and (2) analyze the emotion-based sentiment of the tweets. To identify and analyze the topics, the study used a word list, concordance graphs, semantic network analysis, and topic-modeling approaches. The NRC emotion lexicon was used to examine the sentiment of the tweets. The analysis illustrated that the word “halal” occurred in the highest number of tweets and was primarily associated with the words “food” and “hotel”. It was also observed that non-Muslim countries such as Japan and Thailand appear to be popular as halal tourist destinations. Sentiment analysis found that there were more positive than negative sentiments among the tweets. The findings have shown that halal tourism is a global market and not only restricted to Muslim countries. Thus, industry players should take the opportunity to use social media to their advantage to promote their halal tourism packages as it is an effective method of communication in this decade.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Resource Planning ◽  
Experimental Results ◽  
Quality Prediction ◽  
Classification Models ◽  
Hybrid Features ◽  
Social Media Data ◽  
Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

scholarly journals Twitch user perceptions, attitudes and behaviours in relation to food and beverage marketing on Twitch compared with YouTube

2021 ◽  
Vol 10 ◽  
Author(s):  
Catherine C. Pollack ◽  
Diane Gilbert-Diamond ◽  
Jennifer A. Emond ◽  
Alec Eschholz ◽  
Rebecca K. Evans ◽  
...  
Keyword(s):  
Social Media ◽  
Negative Emotions ◽  
Food Marketing ◽  
Advertising Effectiveness ◽  
Chat Rooms ◽  
User Perceptions ◽  
Future Studies ◽  
Food And Beverage

Abstract Influencer marketing may be amplified on livestreaming platforms (e.g., Twitch) compared with asynchronous social media (e.g., YouTube). However, food and beverage marketing on Twitch has not been evaluated at a user level. The present study aimed to compare users’ self-reported exposure to food marketing and associated attitudes, consumption and purchasing behaviours on Twitch compared with YouTube. A survey administered via social media was completed by 621 Twitch users (90 % male, 64 % white, 69 % under 25 years old). Of respondents, 72 % recalled observing at least one food or beverage advertisement on Twitch. There were significant differences in the recall of specific brands advertised on Twitch (P < 0⋅01). After observing advertised products, 14 % reported craving the product and 8 % reported purchasing one. In chat rooms, 56 % observed conversations related to food and 25 % participated in such conversations. There were significant differences in the number of users who consumed various products while watching Twitch (P < 0⋅01). Of users who frequented YouTube (n 273), 65 % reported negative emotions when encountering advertising on YouTube compared with 40 % on Twitch (P < 0⋅01). A higher proportion felt Twitch's advertising primarily supported content creators (79 v. 54 %, P < 0⋅01), while a higher proportion felt that YouTube's advertising primarily supported the platform (49 v. 66 %, P < 0⋅01). The findings support that food marketing exposures on Twitch are noticeable, less bothersome to users and influence consumption and purchasing behaviours. Future studies are needed to examine how the livestreaming environment may enhance advertising effectiveness relative to asynchronous platforms.

Success Factors of Social Media Applications (SMA): Case Study among UUM Students

2021 ◽  
Vol 12 (3) ◽  
pp. 2733-2745
Author(s):  
Mazlan Mohd Sappri Et.al
Keyword(s):  
Social Media ◽  
Service Quality ◽  
User Satisfaction ◽  
Mobile Application ◽  
Information Quality ◽  
Success Factors ◽  
Reference Model ◽  
Mobile App ◽  
Media Applications

Social media application (SMA) shows several important functions that causing theincrement of usage among mobile application or mobile app users, especially among18 to 28 years-old users. This causing several developers to create their own SMA thathave been targeted to mobile app users. However, only several SMA managed tobecome popular and successful in term of usage, leaving other unpopular SMA in thelower rank of the Google PlayStore. SMA created by developer in Malaysia face thesame situation as mentioned before where those SMA were supposed to attractMalaysian mobile users more. To assess this situation, this study aims to identify thesuccess factors of SMA usage and develop a set of metric based on the success factorsusing research model that have been developed in the past. Information SystemSuccess Model (ISSM) were studied and chosen as the reference model for this studybecause the model is suitable and have been used by other researchers in studiesregarding social media and SMA. ISSM contains several success factors like systemquality, service quality and information quality that affect the user satisfaction and useof a system, where this model were modified in this study with the addition ofnetworking quality and perceive privacy factors. This study were conducted on 380Universiti Utara Malaysia (UUM) students and after analysing the data collected, allproposed success factors except of service quality were found to have a positive impacttowards user satisfaction and usage. The success factors were included in the metricdesign and the metric were presented in an evaluation form for SMA developer inMalaysia to evaluate and applied the metric in their SMA.

Depression Detection on Social Media with Reinforcement Learning

2019 ◽  
pp. 613-624
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Liang Zhu ◽  
Xu Zhou ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Social Media ◽  
Reinforcement Learning ◽  
Depression Detection

scholarly journals Internet Users Engage More With Phatic Posts Than With Health Misinformation On Facebook

2019 ◽  
Author(s):  
Manon Berriche ◽  
Sacha Altay
Keyword(s):  
Social Media ◽  
Social Relations ◽  
Cultural Evolution ◽  
Negative Emotions ◽  
Interdisciplinary Approach ◽  
Emotional Valence ◽  
Real World Data ◽  
Internet Users ◽  
Depth Analysis ◽  
Evolution Dynamics

Social media like Facebook are harshly criticized for the propagation of health misinformation. Yet, little research has provided in-depth analysis of real-world data to measure the extent to which Internet users engage with it. This article examines 6.5 million interactions generated by 500 posts on an emblematic case of online health misinformation: the Facebook page Santé + Mag, which generates five times more interactions than the combination of the five best-established French media outlets. Based on the literature on cultural evolution, we tested whether the presence of cognitive factors of attraction, that tap into evolved cognitive preferences, such as information related to sexuality, social relations, threat, disgust or negative emotions, could explain the success of Santé + Mag’s posts. Drawing from media studies findings, we hypothesized that their popularity could be driven by Internet users’ desire to interact with their friends and family by sharing phatic posts (i.e. statements with no practical information fulfilling a social function such as “hello” or “sister, I love you”).We found that phatic posts were the strongest predictor of interactions, followed by posts with a positive emotional valence. While 50% of the posts were related to social relations, only 28% consisted of health misinformation. Despite its cognitive appeal, health misinformation was a negative predictor of interactions. Sexual contents negatively predicted interactions and other factors of attraction such as disgust, threat or negative emotions did not predict interactions. These results strengthen the idea that Facebook is first and foremost a social network used by people to foster their social relations, not to spread online misinformation. We encourage researchers working on misinformation to conduct finer-grained analysis of online contents and to adopt interdisciplinary approach to study the phatic dimension of communication, together with positive contents, to better understand the cultural evolution dynamics of social media.

scholarly journals An Analysis of Hate Speech among Armenian Facebook Users

2021 ◽  
Vol 7 (1) ◽  
pp. 1-14
Author(s):  
Lilit Bekaryan
Keyword(s):  
Social Media ◽  
Sexual Orientation ◽  
Best Practices ◽  
Hate Speech ◽  
Negative Emotions ◽  
Online Communication ◽  
Passive Voice ◽  
Success Stories ◽  
Language Analysis ◽  
Facebook Pages

Social media networking websites have become platforms where users can not only share their photos, moments of happiness, success stories and best practices, but can also voice their criticism, discontent and negative emotions. It is interesting to follow how something that starts as a mere disagreement or conflict over clashing interests or values can develop into a hateful exchange on Facebook that targets social media users based on their gender, religious belonging, ethnicity, sexual orientation, political convictions etc. The present research explores how hateful posts and comments can start among Facebook users, and studies the language means employed in their design. The factual material was retrieved from more than ten open Facebook pages managed by popular Armenian figures, such as media experts, journalists, politicians and bloggers, in the period 2018–2020. The analysis of hate speech samples extracted from these sources shows that hate speech can find its explicit and implicit reflection in the online communication of Armenian Facebook users, and can be characterised by contextual markers such as invisibility, incitement to violence, invectiveness and immediacy. The language analysis of the posts and comments comprising hate speech has helped to identify language features of hateful comments including informal tone, use of passive voice, abusive and derogatory words, rhetorical or indirectly formed questions, generalisations and labelling.

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