Unlocking Emotional Waves: How Time, Location, and Context Shape Social Media Sentiments

By Jin Chen

Details

In recent years, social media has become an important part of our daily lives, serving as a platform for people to express their emotions and share information. Users express their feelings and emotions through text, images, videos, etc. on social media platforms, including Facebook, Instagram, Twitter, and TikTok. In view of this trend of emotional expression, we have to consider the relationship between the emotional flow of social media and the emotional state of users. How do time, place, and context affect users’ emotional expression?

Picture From The Silicon Review

Social media is both a microcosm of society’s emotions and a window into individuals’ emotional states. From morning commuters’ anxiety to evening athletes’ sense of accomplishment, there are deep social and psychological reasons behind consumers’ emotional swings. In addition to revealing universal patterns of human emotion, these changes also provide useful information and insights for public policy, brand marketing, and even mental health treatment.

Mood swings: ‘Emotional morning peaks’ and’ night lows’ on social media

I discovered a resource showing (from Kaggle dataset) that mood fluctuations on social media have a clear temporal character by examining sentiment data from several platforms, including Facebook, Instagram, and Twitter. In general, mornings are depressing, especially between 6:00 to 9:00. The majority of user-shared posts discussed “difficult commutes” or “morning fatigue.” As numerous users have noted, the hashtags “#Traffic” and “#MorningBlues” are frequently used during this season, illustrating how hectic morning routines affect users’ emotions.

The user’s mood steadily leveled down by 12 or 14 p.m. This time period’s dynamics typically exhibit a high degree of emotional neutrality, and many users will share the status of “lunch time” or “short break,” which reflects the adjusting pace of life and work.

However, the mood fluctuations start to be strongly tied to personal activities in the afternoon and evening (15:00 to 19:00). During this time, a lot of users submit content on socializing, travel, fitness, and other topics. The majority of the feelings at this time were good in comparison to previous periods. For instance, a lot of users discuss their post-workout problems and successes using hashtags like “#Fitness” or “#Workout” to convey a positive and self-assured attitude.

The night turns into a complicated emotional crossroads. Long work or school hours might cause stress for some users, who post depressing feeds like “#WinterBlues” or “#SickDay.” However, other users choose to spend their evenings alone, posting peaceful, introspective content like “#MentalHealth” or “#Reading.”

Geographic differences: Cultural diversity of global sentiment

Geographical location has an impact on social media emotional expressiveness in addition to time. According to the analysis, consumers’ emotional expression varied significantly among nations and regions. In the US, for instance, user opinion is typically favorable, particularly during holidays or significant occasions. American users have a high need for social contact and self-expression, as evidenced by their propensity to share news about personal accomplishments, family get-togethers, and travel on social media.

Canada, on the other hand, has rather more conservative user opinion, particularly in the winter. Extended winters and cold temperatures tend to increase the proportion of unpleasant feelings. Seasonal mood changes are reflected in a lot of posts about the winter blues.

However, Research by Dalhousie University reveals differences in the emotions expressed in COVID-19-related tweets by users in the United States and Canada. Statistical analysis shows that Canadian users’ tweets show a more pronounced tendency towards positive emotions, with sentiment polarity scores fluctuating between 0.018 and 0.074, while American users’ tweets are relatively more negative, with polarity scores consistently below 0.005. In terms of topic selection, American tweets frequently mention political figures and are associated with negative emotions, while Canadian tweets focus more on discussing the government’s response to the epidemic. Regional-level analysis shows that New York State in the United States, as the center of the early epidemic, has more negative emotions in related tweets, while the emotional responses of different provinces in Canada also differ, such as Manitoba and Atlantic Canada, which have a stronger emotional response to the increase in cases.

Event-driven: The emotional impact of holidays and social hotspots

Mood swings on social media are frequently triggered by particular occurrences. Hot social gatherings and holidays can have a big impact on how people express their feelings. For instance, during the January 2023 winter holidays, a large number of people posted updates on travel, celebrations, and family get-togethers. These updates were largely positive and demonstrated the happiness and relaxation that the holidays offered.

However, socal gatherings and trending subjects often cause strong emotional fluctuations. For instance, social media emotional outbursts related to political debates or social movements are frequently severe and significantly more negative. 

Implications of sentiment analysis: Responsibilities and Opportunities for social platforms

Societal media platforms have significant societal obligations as conduits for information sharing and emotional expression. In addition to aiding in the understanding of shifts in people’s mental states, user mood swing analysis can yield important information for public policy, brand marketing, and mental health interventions.

First, in response to shifts in user attitude, the platform can offer tailored content recommendations. When users are depressed, the platform can assist them manage their emotions and prevent overindulging in emotions by assessing their emotional state and presenting more uplifting information. For instance, the platform may suggest activities or warm, comforting content to users who are experiencing low moods throughout the winter months in order to assist them cope with seasonal emotional problems.

Second, social platforms can use emotional data analysis to detect and address the spread of negative emotions, particularly during social hot events. By facilitating logical conversations and encouraging the spread of mental health subjects, early identification of emotional swings on social media platforms might slow the propagation of bad feelings.

Last but not least, sentiment data analysis enables firms to precisely target customer demands and create more emotionally compelling marketing campaigns. For instance, marketers can utilize social media to provide fitness-related positive energy content that speaks to users’ good feelings in response to the fitness boom at the start of the year. This will increase user engagement and brand loyalty.

Data source description

The Social Media Sentiment Analysis Dataset, which Kashish Parmar assembled and made available on the Kaggle platform, served as the basis for this work. Text content, sentiment tags (positive, negative, and neutral), time stamps, hashtags, and interactive data like likes and retweets are all included in this dataset of user-generated content from social networking sites like Facebook, Instagram, and Twitter. I pre-processed the data for the analysis, which included examining the sentiment distribution in relation to geographic regions and analyzing time stamps to find trends over various time periods. By classifying mood tags, we may investigate how a user’s mood varies in response to various activities (such working out or exercising) or outside influences. The features of emotional expression on social media and how it varies among platforms and demographics are presented in detail in this investigation.

Social Media Plan

To ensure that this post receives a lot of attention and encourages user engagement on social media, I will use Facebook, LinkedIn, Instagram, and Twitter, using the following thorough strategy for each platform’s features:

1. Twitter：
2. Post a sequence of tweets with visual material that gradually reveals important results. For instance, “What time of day are users most emotionally positive?” accompanied by a chart of time trends.
3. To improve search exposure, use trending and topic-related hashtags such as #digital psychology, #datavisualization, and #social media sentiment.
4. Direct material to brand marketers, data scientists, and psychologists, asking them to comment on articles or take part in discussions.
5. Ask questions like “Do you think social media sentiment trends reflect real life?” to start frequent, interactive conversations.
6. Instagram：
7. Use color and data contrast to draw the eye to well-designed infographics that incorporate emotional trends over time, the connection between user behaviors and feelings, etc.
8. Use short videos called Reels to illustrate fundamental concepts of mood fluctuation. For example, use dynamic animations to represent mood swings from various activities throughout the day.
9. Asking “What time period do you find social media most negative?” is one way to incorporate interactive features like polls and quizzes into stories. Additionally, in the follow-up, discuss the results and interpretation.
10. Collaborate with social media, data analytics, or mental health influencers to expand the reach and impact of your material.
11. Facebook
12. To get notice, post in communities about digital culture, mental health, or social media research. A synopsis of the article’s main ideas might be included in the post, along with extra questions to get group members to join in the conversation, like “Have you noticed that the sentiment on social media reflects the trend in real life?”
13. Post interactive films and dynamic infographics that invite viewers to comment or like to share their emotional trends and experiences.
14. Organize online gatherings, like Facebook Live, to discuss article insights with data analysts or psychologists and provide real-time audience queries.
15. Cross-platform integration: 
16. Make sure the content of the article promotion is brand similar across all platforms by using a uniform visual style and tagging (e.g., the same theme color, key sentences, and unified labeling).
17. To further encourage cross-platform conversations, send user feedback from one platform to another and report the status of interactions on a regular basis.
18. Push articles to user groups that could be interested, such as youthful producers, content consumers, and social media analysts, by utilizing social media advertising tools like Instagram and Facebook’s precision targeting ads.