Facebook Status Updates Research Articles

The Identity of a Storyteller in Teachers’ Facebook Status Updates

This paper explores how teachers discursively construct the identity of a storyteller when posting Status updates on Facebook Timelines. Data were generated using participant observation where 29 Timelines belonging to Malaysian secondary school English language teachers were observed throughout a six-month period. A total of 178 teaching-related Status updates were recorded and then analyzed using a discursive analytical approach combining ‘broad discourse analysis’ and Edwards and Potter's (1992) discursive psychology. The analysis shows that most of the Status updates fit the structure of a narrative genre and the teachers use various storytelling and discursive psychology strategies to engage readers. This paper argues that teachers' Status updates are more than just an account of mundane teaching-related experiences but serve as a mechanism for them to construct desirable identities.

Language Codes, Topic Preferences and Identity Construction: The Case of Gendered Language on Social Media

This study on exploring the use of gendered language on social media is a qualitative-quantitative analysis of the male and female differences in the language codes, topic preferences, and identity construction. In-depth understanding of the gendered use of language in Facebook was investigated with a purposive sampling of 90 status updates taken from the posts of respondents who are enrolled in one of the premier public high schools in Cagayan Valley. Anchored on Thematic Analysis by Braun and Clarke (2006), the language codes, topic preferences and gender-based identity construction were explored. Findings reveal that both male and female who are active users of Facebook are multilingual whenever they go online. The discussion on the variety of topics speaks of the diversity of the interest of those involved in the communication platform. The interactivity that Facebook provides makes it a medium through which people define themselves by sharing what is going on in their lives or how they are feeling. This makes Facebook a forum where extension of a person’s real-life identity radiates. Moreover, the use of language by male and female are influenced by their social network in Facebook. Through Facebook status updates, male and female do not only express their emotions but also share details about their daily life activities as to what they were doing or what was going on in their lives at that moment. Thus, trainings and seminars among students and teachers on upholding preservation of culture and language must be part of the curriculum to promote youth empowerment, gender fair language and awareness of cyber-bullying, are hereby recommended to develop communicative competence leading to deeper understanding of one another.

The self online: When meaning-making is outsourced to the cyber audience.

This study examines the cyber audience's perception of social media users' persona based on their online posts from a cognitive meaning-making perspective. Participants (N = 158) answered questions about their personal characteristics and provided their 20 most recent Facebook status updates. Two groups of viewers, who viewed either the text-only or multimedia version of the status updates, answered questions about the Facebook users' personal characteristics. The viewers' perceptions of Facebook users deviated from the users' self-perceptions, although user characteristics that serve social motives were more accurately perceived. Multimedia viewers were more accurate than text viewers, whereas the latter showed a greater consensus. Gender and ethnic differences of Facebook users also emerged in online person perceptions, in line with gendered and cultured characteristics. These findings shed critical light on the dynamic interplay between social media users and the cyber audience in the co-construction of a digitally extended self.

Analysis of Congratulation Strategies by Swahili Speakers in Response to Happy Occasions on Facebook Status Updates

This study investigated congratulatory comments posted on Facebook pages in response to H.E Samia Suluhu Hassan’s historic inauguration as the first female president of the United Republic of Tanzania, to determine strategies used by Kiswahili Facebook users to express their congratulations. Case study design was applied in this study. Data was collected through documentary review, where a sum of 200 congratulatory responses were extracted from selected Facebook accounts. The data were then coded based on Elwood’s (2004) congratulatory framework to obtain frequency and percentage of each strategy. The results showed five strategies for expressing congratulations, arranged from the most frequent to the least frequent: Illocutionary force indicating devices, religious expressions, offers of good wishes, speakers’ expression of their faith to the addressee and expressing encouragement. It is recommended that speakers should be familiar with how to express their congratulations to others based on the addressee’s cultural context as utterances used to express congratulations may differ from one culture to another. Failure to congratulate other people when they have delightful occasion is interpreted as being jealous and uncooperative.

Illocutionary Acts Of The 100 Covid-19 Health Protocol Campaign Posters On Facebook Status Updates (A Netnographic Study)

This study aims to identify the types of illocutionary speech acts contained in 100 poster designs containing appeals for the prevention of Covid-19 and the application of health protocols posted on social media Facebook. This study uses a qualitative descriptive method by applying the data collection techniques of Miles, Huberman, and Saldana, namely data condensation, data display, and conclusion. This study uses Searle's theory of illocutionary speech acts to analyze the collected data. The theory includes the types of illocutionary speech acts, namely directive, assertive, expressive, commissive, and declarative. The results of this study indicate that of the 100 poster designs analyzed, there were 64 posters containing directive speech acts, 27 posters containing assertive speech acts, 8 posters containing expressive speech acts, and 1 poster containing commissive speech acts. The large number of posters containing declarative speech acts indicates that the poster is aimed at hearers or audiences to do or carry out something related to the prevention of Covid-19 and the application of strict health protocols. This is in line with the purpose of the poster, which is persuasive, which means it can influence people who see or read it.

A corpus-based pragmatic analysis of Jordanians Facebook status updates during COVID-19

This study investigates the communicative functions of status updates on Facebook during COVID-19. For this purpose, a corpus of 500 status updates was collected from 100 Facebook users for 90 consecutive days. Subsequently, the data were characterized into five speech acts drawing heavily on Searle's speech act framework, prominent among which are expressives and assertives. Data analysis revealed that status updates could be considered a substantial medium for understanding intended human communication. Various types of speech acts were used with different frequencies and percentages, although people were inclined mostly to use expressive speech acts. The sociocultural variations in conjunction with forming and constructing identities were reflected in the status updates manifested in the current situation of the pandemic, which makes Jordanians appear more humorous than before. This research is significant because studying aspects of a language helps in understanding the hidden motivations, beliefs, ideas, attitudes and identities along with the social, cultural, and political factors, which in turn provides logical solutions for certain problems.

POLITIČKI MITOVI U KOMUNIKACIJI NA DRUŠTVENIM MREŽAMA TOKOM ZDRAVSTVENE KRIZE U REPUBLICI ITALIJI 2020. GODINE / POLITICAL MYTHS IN COMMUNICATION ON SOCIAL NETWORKS DURING THE HEALTH CRISIS IN THE REPUBLIC OF ITALY IN 2020

In this paper we analyze conceptual metaphors used in social network communication. We focus on conceptual metaphors regarding the Covid-19 epidemics in Facebook status updates of the Italian minister of foreign affairs Luigi de Maio. Using a cognitive linguistics approach to text and based on the Conceptual Metaphor Theory we analyze five metaphors: orientational metaphors, as well as the CURING THE VIRUS IS WAR, THE STATE IS A PROTECTOR, and FIGHTING THE VIRUS IS A GAME metaphors. Our main goal is to analyze the ways in which conceptual metaphors manifest and what their political goal is. We hypothesize that conceptual metaphors, seeing as we analyze a specific type of political discourse, are a part of a political machinery whose goal is the promotion of a single political party. Our research shows that conceptual metaphors have an ideological and a political purpose; further more they are completely adequate for the creation of political myths. The results of this paper can be further used in research on the analysis of (political) discourse, specific analysis of conceptual metaphors, and in more detailed research on the communication about the 2020 Coronavirus outbreak.

What Do Customers Share About Eating-Out on Facebook?

Discussing the eating-out experience is long-lasting among people around the world, and with the advent of the internet, customers change the way of sharing dining experience online. This study aims to identify the dominant topics and emotional dimensions on Facebook status updates of large-scale individual accounts, as well as the topic variance by users’ age and gender. With an adaption of the myPersonality dataset on Facebook, topic modeling and emotion analysis are conducted. Findings indicate that five primary topics are composed of food, happy time, dining experience, coffee time, and people. Males and females demonstrate a significant difference in the theme of people. Positive emotional dimensions generally show stronger extents than the negative ones. This study is a pioneer of exploring Facebook individual accounts in the hospitality and tourism field and expands industry practitioners’ comprehension of dominant dining-out themes and emotional responses among distinct consumer groups.

Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety, suicide commitment, and mental disorder, particularly in the young adults who have excessively spent time on social media which necessitates a thorough psychological analysis of all these platforms. This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates. In this paper, we start with depression detection in the first instance and then expand on analyzing six other psychotic issues (e.g., depression, anxiety, psychopathic deviate, hypochondria, unrealistic, and hypomania) commonly found in adults due to extreme use of social media networks. To classify the psychotic issues with the user's mental state, we have employed different Machine Learning (ML) classifiers i.e., Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN). The used ML models are trained and tested by using different combinations of features selection techniques. To observe the most suitable classifiers for psychotic issue classification, a cost-benefit function (sometimes termed as ‘Suitability’) has been used which combines the accuracy of the model with its execution time. The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.

Detecting Multi-label emotions from code-mixed Facebook Status Updates

Objectives: With the growth of Social Media and the increasing use of English-Hindi (Hinglish) in linguistically diverse countries such as India, it is becoming increasingly important to analyse Hinglish-language content on Social Media platforms like Facebook. Prior sentiment and emotion analyses have only focused on single-label classification, ignoring the possibility of coexisting emotions within one instance. By analysing code-mixed Facebook status updates, the study aims to investigate multiple emotions. Method: 15,995 English-Hindi mixed Facebook status updates are annotated with emotions like joy, sadness, anger, fear, trust, disgust, surprise, anticipation, and love. Different pre-processing techniques are used to normalize the noisy data to produce more accurate results. We apply five different multi-level classification algorithms with word-level and character n-gram approaches to test the best classification results. Findings: The results of the experiment indicate that a status update can evoke multiple emotions rather than just one. Precision, recall, F1 score, and accuracy using both Micro and Macro averaging are used to evaluate the performance of different classifiers. As compared to other classification algorithms, the Classifier Chains algorithm with its 2-6-gram approach has the highest accuracy of 86% with a precision of 0.98. As compared to other classifiers, the Classifier Chains algorithm offered better results due to its ability to consider the correlations between class labels. Applications: The article focuses on the multi-label emotion classification task, which examines whether a Facebook status update shows none, one, or more of the nine emotions as outlined by Plutchik’s wheel of emotions. Considering the emotion of a text can support decision-making processes in various ways. Keywords: Social Media; Emotion Analysis; Code-Mixed; MultiLabel 29 Classification; Emojis; Lexicons

Dimensions of Self-Expression in Facebook Status Updates

We describe the dimensions along which Facebook users tend to express themselves via status updates using the semi-automated text analysis approach, the Meaning Extraction Method (MEM). First, we examined dimensions of self-expression in all status updates from a sample of four million Facebook users from four English-speaking countries (the United States, Canada, the United Kingdom, and Australia) in order to examine how these countries vary in their self-expressions. All four countries showed a basic three-component structure, indicating that the medium is a stronger influence than country characteristics or demographics on how people use Facebook status updates. In each country, people vary in terms of the extent to which they use Informal Speech, share Positive Events, and discuss School in their Facebook status updates. Together, these factors tell us how users differ in their self-expression, and thus illustrate meaningful use cases for the product: Talking about what’s going on tends to be positive, and people vary in terms of the extent to which their status updates are short, slangy emotional expressions and topics regarding school. The specific words that define these factors showed subtle differences across countries: The use of profanity indicates fewer school words (but only in Australia), whereas the UK shows greater use of slang terms (rather than profanity) when speaking informally. The MEM also identified English-language dialects as a meaningful dimension along which the countries varied. In sum, beyond simply indicating topicality of posts, this study provides insight into how status updates are used for self-expression. We discuss several theoretical frameworks that could produce these results, and more broadly discuss the generation of theoretical frameworks from wholly empirical data (such as naturalistic Internet speech) using the MEM.

Recognising Personality Traits Using Facebook Status Updates

Gaining insight in a web user's personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user's personality traits from their Facebook status updates. Even with a small set of training examples we can outperform the majority class baseline algorithm. Furthermore, the results are improved by adding training examples from another source. This is an interesting result because it indicates that personality trait recognition generalises across social media platforms.

Predicting Conscientiousness through Semantic Analysis of Facebook Posts

Here we present a method for detecting an individual's level of conscientiousness based on an analysis of the content of their Facebook status updates. Our model is based on the identification of semantic evidence of facets related to conscientiousness; an individual's belief of their control over events around them and their goal orientation. The model achieves a correlation of r=.27 on a subset of the Facebook data published for the myPersonality workshop, with an accuracy of 58.13% for detecting if an individual is above or below the median and 68.03% for those outside of one standard deviation. While we take a narrow approach and identify only one personality trait, the general methodology of directly looking for evidence of traits in an individual's utterances is applicable to discovering models for all of the personality traits.

Two Studies Investigating Gender Differences in Response to Facebook Status Updates

We conducted two studies to examine gender differences in in response to Facebook status updates. The first study surveyed 600 undergraduate students (388 females and 207 males), and analysed males’ and females’ responses to Facebook status updates. Females were significantly more likely to post a public reply than males, and female public replies also contained higher levels of emotional support. There were no significant gender differences in private replies to Facebook status updates. Males showed significantly higher levels of emotional support in private messages than in public replies. There was no significant difference in terms of level of emotional support between females’ public replies and private messages. The second study investigated gender differences in response to Facebook status updates from same gender friends compared to opposite gender friends. We surveyed 522 undergraduate students (216 females and 306 males), and analysed males’ and females’ responses to two Facebook status updates: one from a same gender close friend and one from an opposite gender close friend. Females showed higher levels of emotional support than males to a Facebook status update from a same gender friend. In contrast, there were no significant gender differences in response to an opposite gender friend. Males showed higher levels of emotional support in private replies than public replies to same gender friends. There was no difference in level of emotional support between females’ public replies and private messages. The implications of these findings for explanations of gender differences in language use are discussed

Calling All Facebook Friends: Exploring Requests for Help on Facebook

Past research suggests Facebook use is linked to perceptions of social capital, a concept that taps into the resources people gain from interactions with their social network. In this study, we examine a sample of public Facebook status updates (N=20,000) for instances in which users request a response from their network. These attempts to mobilize resources offer insight into the mechanisms through which Facebook is used for social capital conversion. After identifying mobilization requests (N=856), we categorize them by cost (i.e., effort needed to satisfy the request) and type (e.g., opinion, information, social coordination) in order to describe the prevalence of these requests and the extent to which they require effort on the part of the potential responders. Finally, we examine characteristics of these users and the linguistic characteristics of status updates that contain mobilization requests.

Moral concerns are differentially observable in language

Language is a psychologically rich medium for human expression and communication. While language usage has been shown to be a window into various aspects of people's social worlds, including their personality traits and everyday environment, its correspondence to people's moral concerns has yet to be considered. Here, we examine the relationship between language usage and the moral concerns of Care, Fairness, Loyalty, Authority, and Purity as conceptualized by Moral Foundations Theory. We collected Facebook status updates (N = 107,798) from English-speaking participants (n = 2691) along with their responses on the Moral Foundations Questionnaire. Overall, results suggested that self-reported moral concerns may be traced in language usage, though the magnitude of this effect varied considerably among moral concerns. Across a diverse selection of Natural Language Processing methods, Fairness concerns were consistently least correlated with language usage whereas Purity concerns were found to be the most traceable. In exploratory follow-up analyses, each moral concern was found to be differentially related to distinct patterns of relational, emotional, and social language. Our results are the first to relate individual differences in moral concerns to language usage, and to uncover the signatures of moral concerns in language.

Encoding Text Information with Graph Convolutional Networks for Personality Recognition

Personality recognition is a classic and important problem in social engineering. Due to the small number and particularity of personality recognition databases, only limited research has explored convolutional neural networks for this task. In this paper, we explore the use of graph convolutional network techniques for inferring a user’s personality traits from their Facebook status updates or essay information. Since the basic five personality traits (such as openness) and their aspects (such as status information) are related to a wide range of text features, this work takes the Big Five personality model as the core of the study. We construct a single user personality graph for the corpus based on user-document relations, document-word relations, and word co-occurrence and then learn the personality graph convolutional networks (personality GCN) for the user. The parameters or the inputs of our personality GCN are initialized with a one-hot representation for users, words and documents; then, under the supervision of users and documents with known class labels, it jointly learns the embeddings for users, words, and documents. We used feature information sharing to incorporate the correlation between the five personality traits into personality recognition to perfect the personality GCN. Our experimental results on two public and authoritative benchmark datasets show that the general personality GCN without any external word embeddings or knowledge is superior to the state-of-the-art methods for personality recognition. The personality GCN method is efficient on small datasets, and the average F1-score and accuracy of personality recognition are improved by up to approximately 3.6% and 2.4–2.57%, respectively.

The individual dynamics of affective expression on social media

Understanding the temporal dynamics of affect is crucial for our understanding human emotions in general. In this study, we empirically test a computational model of affective dynamics by analyzing a large-scale dataset of Facebook status updates using text analysis techniques. Our analyses support the central assumptions of our model: After stimulation, affective states, quantified as valence and arousal, exponentially return to an individual-specific baseline. On average, this baseline is at a slightly positive valence value and at a moderate arousal point below the midpoint. Furthermore, affective expression, in this case posting a status update on Facebook, immediately pushes arousal and valence towards the baseline by a proportional value. These results are robust to the choice of the text analysis technique and illustrate the fast timescale of affective dynamics through social media text. These outcomes are of high relevance for affective computing, the detection and modeling of collective emotions, the refinement of psychological research methodology, and the detection of abnormal, and potentially pathological, individual affect dynamics.

English as a We-Code: form and Function of English in Facebook Status Updates of Non-Native Female and Male Users

The English language has featured markedly as a popular language of computer-mediated communication, and notably of Facebook posts, written not only by native or second language speakers, but also users of English as a foreign language. The aim of this paper is to investigate the frequency, form and function of English language Facebook profile updates of 110 (55 women and 55 men) users of English representing 41 European, Asian, African and Latin American countries belonging to the Expanding Circle. Approached from the point of view of the code choice as well as the users’ gender, and supported by an online survey data, the study analyses in detail the form of the updates in connection with gender preferences and identifies language contexts and functions users choose to express themselves in English as opposed to their native tongue, thereby demonstrating the role of English as a we-code in a social networking service.

The Impact of Culture and Gender on the Production of Online Speech Acts among Jordanian Facebook Users

This study of speech acts offers deep insights into the social structure and gender differences of any speech community. Most relevant research on online speech acts has shed much light on Western speech communities, neglecting the speech act behavior of Arabic speaking communities. This study aimed to examine the influence of gender differences and the Jordanian cultural norms on the use of speech acts in Facebook Status Updates (FSUs). A total of 1718 FSUs were collected over a period of 2 months. Then, they were categorized according to Searle’s speech acts taxonomy. Results showed that women made an average of 6 updates to their Facebook status, while men averaged four. In addition, the results revealed that females and males differ in their linguistic repertoires. Male participants were inclined to use more assertive speech acts, whereas females were more likely to use expressive speech acts. Islam and tribalism are factors that were found to play an important role in defining the cultural norms of the Jordanian speech community. .