Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup

Using archived social media data, the language signatures of people going through breakups were mapped. Text analyses were conducted on 1,027,541 posts from 6,803 Reddit users who had posted about their breakups. The posts include users’ Reddit history in the 2 y surrounding their breakups across the various domains of their life, not just posts pertaining to their relationship. Language markers of an impending breakup were evident 3 mo before the event, peaking on the week of the breakup and returning to baseline 6 mo later. Signs included an increase in I-words, we-words, and cognitive processing words (characteristic of depression, collective focus, and the meaning-making process, respectively) and drops in analytic thinking (indicating more personal and informal language). The patterns held even when people were posting to groups unrelated to breakups and other relationship topics. People who posted about their breakup for longer time periods were less well-adjusted a year after their breakup compared to short-term posters. The language patterns seen for breakups replicated for users going through divorce (n = 5,144; 1,109,867 posts) or other types of upheavals (n = 51,357; 11,081,882 posts). The cognitive underpinnings of emotional upheavals are discussed using language as a lens.

Download Full-text

SHORT-TERM LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORKS AND SOCIAL MEDIA DATA

10.1049/icp.2021.1552 ◽

2021 ◽

Author(s):

A. Boyd ◽

H. Sun ◽

M. Black ◽

S. Jesson

Keyword(s):

Neural Networks ◽

Social Media ◽

Artificial Neural Networks ◽

Load Forecasting ◽

Short Term ◽

Social Media Data ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Media Data

Download Full-text

PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5352 ◽

2020 ◽

Vol 34 (01) ◽

pp. 206-213

Author(s):

Tiancheng Shen ◽

Jia Jia ◽

Yan Li ◽

Yihui Ma ◽

Yaohua Bu ◽

...

Keyword(s):

Social Media ◽

Parameter Analysis ◽

Rapid Expansion ◽

Music Recommendation ◽

Short Term ◽

Social Media Data ◽

Social Media Platforms ◽

Latent Representations ◽

Media Data

With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

Download Full-text

Depression Analysis from Social Media Data in Bangla Language using Long Short Term Memory (LSTM) Recurrent Neural Network Technique

2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) ◽

10.1109/ic4me247184.2019.9036528 ◽

2019 ◽

Cited By ~ 2

Author(s):

Abdul Hasib Uddin ◽

Durjoy Bapery ◽

Abu Shamim Mohammad Arif

Keyword(s):

Neural Network ◽

Social Media ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Short Term ◽

Social Media Data ◽

Term Memory ◽

Long Short Term Memory ◽

Media Data

Download Full-text

Parental Mentalizing During a Pandemic: Use of Mental-State Language on Parenting Social Media Before and During the COVID-19 Pandemic

Clinical Psychological Science ◽

10.1177/21677026211062612 ◽

2022 ◽

pp. 216770262110626

Author(s):

Tal Yatziv ◽

Almog Simchon ◽

Nicholas Manco ◽

Michael Gilead ◽

Helena J. V. Rutherford

Keyword(s):

Social Media ◽

Mental State ◽

Text Analysis ◽

Matched Control ◽

Mental States ◽

Social Media Data ◽

Mental State Language ◽

Time Periods ◽

Media Data ◽

State Language

The COVID-19 pandemic has been a demanding caregiving context for parents, particularly during lockdowns. In this study, we examined parental mentalization, parents’ proclivity to consider their own and their child’s mental states, during the pandemic, as manifested in mental-state language (MSL) on parenting social media. Parenting-related posts on Reddit from two time periods in the pandemic in 2020, March to April (lockdown) and July to August (postlockdown), were compared with time-matched control periods in 2019. MSL and self–other references were measured using text-analysis methods. Parental mentalization content decreased during the pandemic: Posts referred less to mental activities and to other people during the COVID-19 pandemic and showed decreased affective MSL, cognitive MSL, and self-references specifically during lockdown. Father-specific subreddits exhibited strongest declines in mentalization content, whereas mother-specific subreddits exhibited smaller changes. Implications on understanding associations between caregiving contexts and parental mentalization, gender differences, and the value of using social-media data to study parenting and mentalizing are discussed.

Download Full-text

Sarcastic user behavior classification and prediction from social media data using firebug swarm optimization-based long short-term memory

The Journal of Supercomputing ◽

10.1007/s11227-021-04028-4 ◽

2021 ◽

Author(s):

E. Karthik ◽

T. Sethukarasi

Keyword(s):

Social Media ◽

Short Term Memory ◽

User Behavior ◽

Short Term ◽

Swarm Optimization ◽

Social Media Data ◽

Term Memory ◽

Long Short Term Memory ◽

Media Data

Download Full-text

A three-layer model on users’ interests mining

Journal of Information Science ◽

10.1177/0165551517743645 ◽

2017 ◽

Vol 44 (1) ◽

pp. 136-144 ◽

Cited By ~ 1

Author(s):

Renfeng Yang ◽

Wenbo Xie ◽

Duanbing Chen

Keyword(s):

Social Media ◽

Big Data ◽

Layer Model ◽

Short Term ◽

Social Media Data ◽

Sina Weibo ◽

Hit Rate ◽

The Social ◽

Media Data

With the advent of big data era, social media plays an important role in many areas such as security and finance. Researchers pay more attention on mining users’ interests through the social media data. A three-layer model (TLM) based on keyword extracting is proposed to mine users’ interests, which includes candidate words extracting, semantic structures analysing and interest words ranking. The TLM mainly focuses on both self-importance and semantic-importance of interest words. In addition, the TLM also considers the interest drifting to track long-term and short-term interests of users. Experiments conducted on 10 SINA Weibo datasets show that TLM is more efficient than existing methods to identify users’ interests based on hit rate.

Download Full-text