Verbal Claim vs. Actual Usage of Social Media: A Case Study of Indians Aged 50 Years and Above

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Studies have revealed how millennials use social networking sites (SNS) but users aged 50 years and above have not been studied by SNS researchers in India. This paper attempts to study the verbal claim and actual content created by Indians’ aged 50 years and above on Facebook (Facebook, hereafter referred to as the “Social Media” or “SM”, is restricted by the Russian media regulator Roskomnadzor) using a comparative research design. The study was carried out by selecting one hundred profiles of Monthly Active Users (MAU) of Social Media (SM) qualifying the age criteria. Empirical evidence for comparison was drawn by conducting: a. guided telephonic interview to know MAU’s claimed SM usage and b. quantitative content analysis to identify MAU’s actual SM activity. The results show that the participant’s claims of their SM activity do not support their actual usage of the platform. The variables studied and compared include the ‘source’, ‘context’ and ‘features used’ to write the content. Conclusions reveal the dilemmas and SNS usage related preferences of older age groups in India. The study highlights the need and scope for training the older generation(s) about using SNS. Such vocational training would help them conveniently adopt and use networked technologies for communication and recreational purposes.

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Introduction Many studies on social networking sites (SNS) have emerged highlighting usage patterns of millennials but have ignored the older generations or have clustered them under a single wider age bracket e.g., 35 years and above (We Are Social; Hootsuite, 2020). As per population statistics of the year 2020, nineteen per cent of the total Indian population i.e., 267.5 million Indians are estimated to be aged 50 years and above (World Population Prospects: The 2017 Revision, 2017). Also, because the average retirement age in the country is 5860 years (Times News Network, 2018), it means that an Indian aged 50 years must be at the peak of his career and planning his post-retirement life. It also justifies them as a reservoir of skills and knowledge because of their wide range of experience. Oddly, in India these individuals are an untapped human resource, not being adequately utilized to fill the gap in skills or knowledge required by the subsequent generations (Tarkar & Dhamija, 2020). There may be a lot of reasons for it, but SNS like SM allow to increase networking opportunities. It makes creating new connections and retaining the older ones much more convenient (Sinclair & Grieve, 2017). Also, such online connectivity can do eliminate their feeling of loneliness which is heightened by lack of social interaction and poor health condition (Agewell, 2017). Various tools and features of SM can be extremely beneficial for them to be in touch with their network of friends, colleagues, family members and acquaintances. It also allows them to keep up with the social lives of their kids and grandchildren (Sweney, 2018). Considering their willingness to be digitally literate, the study conducted on Indians aged 60 years or more revealed that 53.21% of participants strongly felt the need for computer training and 52.60% ranked social interaction as its topmost benefit (Agewell, 2017). In India their role as opinion leaders and decisionmakers of various community groups and social institutions (Chadha, 1995) has always been pivotal. Therefore, SNS can boost sharing their views, knowledge and concerns with public, but very little is known about creation of online content by them and familiarity with such platforms (Sinclair & Grieve, 2017). It has been proven that rise in technology and digital media penetration makes these individuals opt for SNS to connect with broader audience (McLachlan, 2021). According to 2018 reports, SM’s monthly active user[57] (MAU) base in India comprised 19% of the total Indian population and 4% out of these users were aged 50 years and above (Digital in 2018…, 2018). While now after two years the MAU base has widened to 28.9% of the total Indian population i.e., 410 million and almost 9-10% of these users i.e., approximately 38.9 million are aged 50 years and above (Napolean Sp., 2021). The almost doubled-up size strongly suggests the need of exploring the SNS usage of these individuals. Participation of seniors on SM is also becoming a global trend, even if their population is smaller in size, but they flock to SM more than any other SNS (Pew Research Center, 2021). This makes it even more important to identify their perception and usage of SM, which is what this research attempts to explore. The study identifies and compares their perception of using the SNS platform (i.e., SM) with their actual online activity on it. The comparison explains their apprehensions, understanding and purpose of using SNS. It also suggests their preference of a. SNS features, b. context and c. sources they would opt for writing a post[58]. The conclusions reveal the gap between their perception and actual usage of the platform while highlighting their need to be proficient in using the same. The results reveal that their SM posts can transform not only their lives but also their connections residing within and beyond India. Conceptual Framework. The study to understand SNS preferences and usage of Indians aged 50 years and above was exploratory in nature. It was carried out using comparative research. The design enabled measuring and comparing verbal claims of the selected SNS users (i.e., their perception of SM activity) with their actual usage of the platform. Platform Selection. The Global Digital Report 2020 (We Are Social; Hootsuite, 2020, p. 95) served as the basis to select the platform. The report highlighted ‘SM’ as the world’s most used social media platform with 2,449 million MAUs. The country-specific section indicated that SM was the most used SNS platform in India. Also, the website, i.e.,, invited the maximum average monthly traffic (Similarweb, 2020) in the social media category in India as of January 2020. Hence, SM was chosen from the available SNS platforms to carry out the analysis. Participants. A combination of two sampling techniques i.e., convenience and snowball sampling were used to select a total of 100 participants (N = 100). The qualification criteria for the participant were: a. aged 50 years or above as of January 1, 2021, and b. MAU of SM. Keeping in view SM’s content privacy policy, the researchers could only reach out to their SM friends[59] for research. Hence, the first 50 participants were selected from the researcher’s SM friendlist, who successfully met the eligibility criteria. These selected participants were then asked to recommend another participant from their respective SM friendlist. Thus, the next 50 participants comprised a snowballed sample. Procedure. Two following methods were adopted to gather data from each participant: Telephonic Interview. A telephonic interview was carried out to gather verbal claims about the recent five SM posts made by the participants. It was conducted using the guide. The data was based on three measures: a. source of content, b. context of content and c. create post features (refer Measures for details) with each having five discrete categories seeking participant’s response. A 6-point Non-Likert Scale (0-5) with never (0), rarely (1), sometimes (2), many times (3), most of the times (4) and always (5) as the options was used to identify participant’s perceived preferences. The researchers also sought a permission from each participant to access their SM profile[60] to carry out a quantitative analysis as the second step of their data collection. Quantitative Content Analysis. In order to find and collect data about the participant’s actual SM activity, the researcher sought permission from each participant to access their respective SM posts. The content of the selected posts was quantitatively analysed against three measures: a. source of content, b. context of content, and c. create post features. It is important to note that none of the participants denied permission meaning N = 100 and Missing Values = None. To minimize the chances of duplication caused by SM’s infinite scroll[61], it was decided that only first five posts appearing on each participant’s wall will be utilised for the analysis. Hence, a total of 500 posts (i.e., 100 participants*5 posts = 500 posts) were used in the analysis carried out using the frequency count ranging from 0-6 ordinal numbers for each of the created codes. Measures. The researchers identified three areas of concern to understand the content posting[62] patterns of the participants. Frequency count of these areas could act as a measure to know content related preferences of these individuals. A comparison of the values measured through verbal claims and participant’s actual activity further helped predict how well-versed were these participants with the selected SNS features. The three measures undertaken are as follows: Source of Content. The source of the content posted on SM by the participants was identified using 5 discrete categories: self-authored, friendauthored, advertised, outsourced, and other content. Self-authored content referred to the content posted directly by the participant, whereas friendauthored meant the content shared by the participant from his or her SM friend’s wall (Scott, 2014). Sponsored content includes posts from SM business page owners trying to increase their post reach among SM users for a fee. Such posts are clearly labelled as ‘sponsored’ by SM (Chandler & Munday, A Dictionary of Social Media, 2016). Posts with links to webpages not hosted by SM (banned in the RF) were counted as outsourced content, and those that did not qualify as any of the categories mentioned above were included under the other category. Context of Content. The context was studied by classifying posts made by the participants in another 5 distinguished categories, i.e., news-related, personal, entertainment, promotional and motivational. The posts containing information like recent political happenings, national & policy-related issues, and events were categorised as news-related; while those having content related to participants’ individual lives, thoughts, and family-related events were considered as personal. The posts with memes, cartoons, songs, poems, stand-up comedy videos or any such content inciting humour were identified as entertainment. Call for action[63] posts were categorised as promotional, and those inciting feelings of self-belief, inspiration and dedication towards life or career were included under the motivational category. ‘Create post’ Features. A total of five features included in the ‘create post’[64] dialog-box were selected, namely, background formatting, add photo or video, tag a friend, feeling or activity and tag a location. It helped study the participant’s familiarity with features related to writing a post. The criterion for selection of these features was based upon their first appearance clockwise on clicking ‘what’s on your mind’ (or ‘create post’) dialog box. After seeking a verbal response of the frequency of using each of the selected features in the telephonic interview, the actual usage of the feature was checked using quantitative analysis of their posts. Data Analysis. Since the study doesn’t account for the nature of frequency distribution, non-parametric test was used for analysis. Mean and median values were calculated for all the categories of the three measures (namely, source, context, and ‘create post’ features). The mean values denoted as [µverbal] represent those gathered through telephonic interviews, while [µactual] represent the mean calculated by quantitative data analysis. The values were then compared to confirm if [µverbal] = [µactual] as the study involves a related sample. On the other hand, while analyzing the ordinal data, median values ensured zero-effect of extreme scores or skewness in frequency distribution (Field, Center of a Frequency Distribution, 2009). The analysis was carried out using the related sample sign test with [µverbal] as reference value, p-value = 0.05 at 95% confidence interval (Field, Non-Parametric Tests, 2009). Z-values were indicative of under or overestimation of verbal claims in comparison with actual activity of these participants. A negative z-value suggested that verbally the participants overclaimed (i.e., claimed too much or exaggerated) their frequency of using that particular category whereas, a positive z-value reflected an under-claim (i.e., claiming an insufficient amount) about the same. The gaps were further analyzed for implications and recommendations about enhancing the SNS experience of such users. The analysis also resulted in highlighting key factors that must be taken care of while enabling these individuals to optimally utilize the vast set of features offered by such sites. Results. The related-sample sign tests were conducted for each of the five categories of the three measures (refer Measures under the Methods section) to determine the statistical significance of participants’ verbal claims compared to their actual usage. The results of the fifteen paired-sample sign tests (as mentioned in Table-1) suggest: a) source of the content: Participant’s verbal claims about self-authored, friend authored, advertised, outsourced, and other contents are significantly different (p < 0.5) from the source used for writing their actual posts. The median verbal claims were higher or equal to the median of actual usage for friend authored, advertised, outsourced and other content. Simultaneously, a lower median value of the verbal claims for self-authored posts than its corresponding median for actual usage justifies the lesser negative value of the z-statistic; b) context of the content: Participant’s verbal claims about all five categories, i.e., news, personal, entertainment, promotional and, motivational, differ significantly (p < 0.5) from the actual context of their SM posts. The median value of the verbal claims for each category was also found to be lesser than its corresponding median value for actual usage. A lower negative value of zstatistic for personal and motivational categories indicates that the verbal claims were close to the actual usage of the two contexts; c) features used while posting: Participant’s verbal claims for four categories, namely, tag a friend, tag a location, feeling or activity and, background formatting, are significantly different from their actual usage of these features in their SM posts. While the feature titled add a photo or video showed no statistically significant difference (p > 0.5) from the verbal claims made by the participant. This further means that the difference of median values calculated for verbal claims and actual usage of add a photo or video is zero. Discussion. The related sample comparison about source of content suggested that despite self-authoring most of their SM (banned in the RF) posts, the participants didn’t verbally acknowledge themselves (i.e., self-authored content) for writing the posts. A higher negative value of z-statistic for friend-authored content confirmed that their verbal claim about sharing the posts authored by their SM friend was an absolute exaggeration. The only source of content that the participants foretold accurately was the advertised posts. It also confirmed that the participants were not just able to identify the advertised content but also possessed a heightened sense of awareness towards sharing the same on their wall. Table 1 Results of related sample sign tests (N = 100) for the categories of: a. Source of Content, b. Context of Content and c. Create Post Features Exact Z- Signifi- Variable µverbal SD Median (50th) µactual SD Median (50th) value (2-p-value tailed) cance Self-Authored 1.92 1.24 2.0 2.72 1.86 3.0 -2.17 0.02* p < 0.05 Friend Authored 2.11 1.25 2.0 0.15 0.50 0.01 -9.11 0.001* p < 0.05 Advertised 1.50 1.14 1.0 1.36 1.64 1.0 -1.45 0.13* p < 0.05 Outsourced 1.30 1.25 1.0 0.30 0.85 0.01 -6.68 0.001* p < 0.05 Other 0.55 0.64 0.01 0.47 1.35 0.01 -4.02 0.001* p < 0.05 News 2.74 1.15 3.0 0.56 1.03 0.01 -8.12 0.001* p < 0.05 Personal 2.99 1.07 3.0 2.01 1.72 2.0 -4.28 0.001* p < 0.05 Entertainment 3.10 1.02 3.0 0.87 1.06 1.0 -9.33 0.001* p < 0.05 Promotional 3.00 0.99 3.0 0.11 0.34 0.01 -9.70 0.001* p < 0.05 Motivational 2.72 1.01 3.0 1.45 1.41 1.0 -5.52 0.001* p < 0.05 Photo / Video 2.84 1.11 3.0 2.67 1.85 3.0 0.00 1.00 p > 0.5 Tag a Friend 3.15 1.00 3.0 0.48 1.02 0.01 -8.82 0.001* p < 0.05 Tag Location 3.24 1.09 3.0 0.22 0.74 0.01 -9.54 0.001* p <0 .05 Feeling / Activity 3.37 1.16 3.0 0.15 0.57 0.01 -9.99 0.001* p < 0.05 Background Formatting 3.85 1.10 4.0 0.11 0.46 0.01 -9.84 0.001* p < 0.05 µverbal = Mean Value of verbal claims, µactual = Mean Value of actual claims, SD = Standard Deviation, Confidence Interval = 0.95 Significance is calculated using exact p-values for the 2-tailed test. *Statistically significant difference. The values above are calculated using IBM SPSS Statistics 21 software. Discussion. The related sample comparison about source of content suggested that despite self-authoring most of their SM posts, the participants didn’t verbally acknowledge themselves (i.e., self-authored content) for writing the posts. A higher negative value of z-statistic for friend-authored content confirmed that their verbal claim about sharing the posts authored by their SM friend was an absolute exaggeration. The only source of content that the participants foretold accurately was the advertised posts. It also confirmed that the participants were not just able to identify the advertised content but also possessed a heightened sense of awareness towards sharing the same on their wall. In terms of the context of content, their claims for every category exceeded the actual activity. Except for personal content marginally exceeding the claim, the rest four categories, i.e., motivational, entertainment, news and promotional, were less opted for creating SM posts. Motivational content ranked second after personal content in terms of both their claims and the actual activity. The findings related to personal and motivational content reinstate the 5-stage ‘Hierarchy of Needs’ model (Maslow, 2013) by suggesting that self-actualization follows the fulfilment of esteem related needs. For all the categories of the source and context of the participants’ SM posts, it was found that µverbal ≠ µactual. The participants without acknowledging themselves as authors of their posts, believed that they share their friend’s content on their wall. In contrast, the study showed how frequently the participants are creating their personalized content. It confirmed the idea of ‘too many comparisons’ (Mukherjee, 2018) because the participants either felt inadequate or were ignorant towards their own interactive skills. They further believed that their online friends shared more or better content as opposed to themselves. Regarding context, they were unable to differentiate news from promotional and entertainment content. This may have serious implications for the news industry. Their understanding of motivational content also appeared unclear. Regarding the create post features, except for adding photos and videos, the participants showed reluctance to use the features despite claiming otherwise. It suggests that the participants could easily recognize most of the features yet refrained from using them. Limitation and further research. Restricting the telephonic interview to abide by the schedule strictly caused a lot of time constraint. Seeking written permissions to access and review the participants’ SM posts caused delays; hence, it was sought over the phone. The feature of infinite scrolling on SM caused the user data to reload multiple times while culling out participant’s posts. The researcher included individuals with a diverse range of professions, including pensioners, retirees, working individuals, homemakers and ad hoc employees, to rule out the effect of their work culture on the context of content shared by them. Further enquiry on this subject could be designing a comprehensive awareness or guidance program helping elderlies to optimally use SNS. It would also be advisable to see what are the privacy concerns of these individuals. A preand post-program comparison could be conducted to see and improve the efficacy of such drives. It would also indicate how Indian policymakers can involve these individuals to meet the Digital India initiative’s goal of ‘universal digital literacy’ (Ministry of Electronics and Information Technology, 2019) and utilise their seemingly effective SM networks to achieve the same. Conclusion. On combining the outcomes of studying the content’s source, context and features used, Indians aged 50 years and above emerge as the new league of content creators because most of the content shared by them is selfauthored, and contextually it is either personal or motivational. Further, adding photos and videos to most of their posts promises their content to be engaging. It further signifies them as potential social media influencers with ample real-life experiences to learn from and share. The study strongly recommends an awareness drive to better equip such individuals with social media tools and features. The guidance must include privacy issues to boost their confidence in owning and safeguarding their social media content. A comprehensive digital awareness and computer training program must be created to cater to their social networking requirements (Embedding Technology to Embed Skills, n.d.). Also, their actual

About the authors

D. Prachi

University School of Mass Communication, Guru Gobind Singh Indraprastha University

ORCID iD: 0000-0003-3487-434X

PhD Scholar

Sector-16 C, Dwarka, New Delhi-110078, India

Chandrakant P. Singh

University School of Mass Communication, Guru Gobind Singh Indraprastha University

ORCID iD: 0000-0002-9635-6882

Professor, UNESCO Consultant on Model Journalism Curriculum in South Asia.

Sector-16 C, Dwarka, New Delhi-110078, India

Irina I. Volkova

Peoples’ Friendship University of Russia (RUDN University)

Author for correspondence.
ORCID iD: 0000-0002-2693-1204

Doctor of Philological Sciences, Professor

10 Miklukho-Maklaya St, bldg 2, Moscow, 117198, Russian Federation


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