Strategic Marketing

STRATEGIES FOR IDENTIFYING AND ALLEVIATING FAKE NEWS ON SOCIAL MEDIA IN NIGERIA

Advanced Management, People Management, Strategic Marketing, Strategy Development /
BY
 
EKOH, Favour Ozioma
Learn to Live Business School, UK
 
 
Correspondence: favourekoh6@gmail.com
Phone No.: 0703036 8401

Acknowledgments

Acknowledgment is hereby given to Dr Ugochukwu Okorozoh for his immense contribution to this project.

ABSTRACT

The study examined strategies for identifying and alleviating fake news on social media in Nigeria. The study adopted the simple linear correlation research design. The study employed a cross-sectional survey approach and a five-point Likert scale questionnaire was used to elicit responses from 385 respondents from the selected social media tools using a purposive sampling technique which was used to ensure full representation of each of the strata under study. The Cronbach Alpha statistic was used to obtain index coefficient values of 0.821 for the dependent variable and 0.894 for the independent variable as the instrument reliability ratio. The dataset was first subjected to a normality test for the residual term using the Kolmogorov-Smirnov Statistic, but the result revealed that the normality assumption was not satisfied for the three independent variables; hence the introduction of the parametric regression analysis technique (Theil regression) was appropriately employed. The research questions were answered using the Spearman rank correlation coefficient to establish the relationship between the dependent and independent variables in the study. The hypotheses were tested with the Theil regression technique to measure the “significance” of the degree of relationships existing between the dependent and independent variables. The analysis was enabled by the use of R-Studio and Minitab software packages. The study concluded the effectiveness of fact-checking training, AI-powered fake news detection tools, and collaboration between social media platforms and fact-checking organizations in reducing the spread of misinformation. The study recommended among others that social media platforms should implement AI-powered fake news detection tools to reduce the spread of misinformation.Keywords: Fake news, social media, Nigeria, media literacy, fact-checking, misinformation

INTRODUCTION

Information fabrication otherwise known as ‘fake news’ is not new. Misinformation, disinformation, and propaganda have been features of human communication, but never before, has there been a technology that effectively disseminates it than social media. Standage (2017) believes that “fake news has been known since the earliest days of printing however, the proliferation of fake news on social media has become a significant concern globally, with Nigeria being no exception (Ojebuyi & Ukpabi, 2020).

Fake news and hoaxes have been a thing over time, and have been trending even before the advent of the Internet. Fake news in the information world are fictitious articles created and spread by rumor mongers that deliberately deceive readers knowingly or unknowingly. Unfortunately, social media and other fake news outlets publish and circulate hoaxes to increase readership and sales of their commodities and generate revenue for economic gains and benefits. Their acclaimed benefit in many cases is a disadvantage to society. Finally, the arrival of the Internet in the late 20th century, followed by social media in the 21st century unimaginably escalated the dangers of misinformation, disinformation, propaganda and hoaxes”. In Nigeria, fake news – intentional or unintentional is equally not new. For instance, in November 1989, the Nigerian Television Authority (NTA) announced the death of the first Governor General and President of Nigeria, Dr Nnamdi Azikwe. By the next morning, the news was on the front pages of most of the country’s newspapers. It took two days before Dr. Nnamdi Azikwe cleared the air about his health status and informed the world that he was still alive and the false claim was relatively contained. Thirty years later, rumors circulated that General Mohammadu Buhari had died during one of his lengthy absences from Nigeria in 2017 on medical grounds and that he had been replaced by a clone called Jubril from Sudan. The supposed death of General Mohammadu Buhari in contrast spread like wildfire on Twitter, Facebook, WhatsApp, and many other social media platforms so much so that he had to address the claim at a news conference (News Wires, 2019).     Indeed, both errors and fraudulent content now go viral through peer-to-peer distribution, and news satire is regularly misunderstood and re-shared as straight news by unwitting social media users. Hence, we now live in a world with computational propaganda, state-sponsored ‘sock-puppet networks’, troll armies, and technology that can mimic legitimate news websites and seamlessly manipulate audio and video to create synthetic representations of any number of sources (Posetti & Mattews, 2018).  The spread of fake news on the Internet is further necessitated by the internet’s ever-growing and connected nature and the preference for speed over accuracy and impact. Because internet content providers and distributors are in a zero-sum, winner-takes-all battle for attention and patronage, they do all within their reach to boost traffic. Unlike the print media which exercise due diligence before reporting any news, online media are usually overwhelmed by the frenzy of the opportunities of the new technology so much so that (Adeleke, 2016) alleges that, many journalists would rather publish first and verify later. This scenario is fuelling the spread of fake news in Nigeria and the world with its accompanying negative security implications.

To achieve the set objective of this study, it will focus attention on social media platforms: Facebook, Twitter, WhatsApp, Instagram, and YouTube, types of fake news:  and other local initiatives, media literacy programs: Educational institutions, civil society organizations, and government agencies to contribute to the development of effective strategies for identifying and alleviating fake news on social media in Nigeria, and promote a more informed and critical online community.

1.1       Statement of the Problem

The proliferation of fake news on social media in Nigeria has become a significant threat to national security, political stability, and social cohesion despite efforts to combat misinformation. Fake news continues to spread rapidly, often with devastating consequences. The lack of effective strategies to identify and alleviate sham news on social media has led to increased polarization and erosion of trust in institutions, manipulation of public opinion and electoral processes, incitement of violence and communal conflicts, undermining of credible journalism and fact-based reporting, erosion of social cohesion and national unity.

It is for these issues, consequences, challenges, other gaps, and the need for effective strategies that this study is meant to address with more focus on developing effective solutions to mitigate the spread of misinformation and promote fact-based reporting.”

1.2       Objectives of the Study

The primary objective of this study is to identify effective strategies for identifying and alleviating fake news on social media in Nigeria, through the following:

i.          Investigate the effect of fact-checking training on social media users’ ability to identify fake news;

ii.         Examine the effectiveness of AI-powered fake news detection tools in reducing the spread of fake news;

iii.        Investigate the impact of collaboration between social media platforms and fact-checking organizations on reducing fake news.

1.3       Research Questions

The study was guided by the following research questions:

  1. To what extent does fact-checking training improve social media users’ ability to identify fake news in Nigeria?
  2. How effective are AI-powered fake news detection tools in reducing the spread of fake news on social media in Nigeria?
  3. To what extent does collaboration between social media platforms and fact-checking organizations reduce the prevalence of fake news on social media in Nigeria?

1.4       Research Hypotheses

H01: There is no significant difference in the ability to identify fake news between social media users in Nigeria who receive fact-checking training and those who do not;

H02: The use of AI-powered fake news detection tools will not reduce the spread of fake news on social media in Nigeria;

H03: Collaboration between social media platforms and fact-checking organizations will not lead to a significant reduction in fake news on social media in Nigeria.

2          LITERATURE REVIEW

2.1       Fake News: a Conceptual Clarification

The phrase “fake news” comprises two simple words; ‘fake’ – meaning, something not genuine, but meant to be taken as authentic; and ‘news’- meaning, information about current events. Therefore, one can literarily say that fake news is information/news that is not genuine which is presented as, and is expected to be believed as being authentic. However, this definition may seem too simplistic and narrow, so we explore available literature for the opinions of other scholars from various backgrounds on the concept of fake news. According to McGonagle (2017), fake news is information that has been deliberately fabricated and disseminated to deceive and mislead others into believing falsehoods or doubting verifiable facts. In this regard, to the information professional, fake news and hoaxes are disinformation that is presented as, or is likely to be perceived as news. A type of yellow journalism or propaganda that consists of deliberate disinformation or hoaxes spread via traditional media or online social media. Also, Alawode, Olorede, and Azeez (2018) view fake news as news articles that are intentionally and verifiably false and could mislead readers. The authors explained that fake news includes false information that can be verified as such, created with dishonest intentions to mislead readers. So far, the core deducible elements of fake news are the ‘falseness’ of news content and the intent to deceive or mislead. Therefore, false content is created in error and circulated without the intention of misleading the consumers and may be dismissible since no human system is immune to mistakes.  It is against this background that Claire Wardle cited in Ogbette, Idam, Kareem, and Ogbette (2019), discussed seven types of fake news.

Misinformation

2.1.1 Types of Fake News

Satire/Parody
Disinformation 

Misinformation

Propaganda
Deepfakes
Clickbait
Hoaxes

 

Fig.1. Different

 Source: Wardle, C. (2017). Fake news: It’s complicated. First Draft News

1. Misinformation: False or inaccurate information spread without the intention to deceive.

2. Disinformation: False information spread to deceive or manipulate.

3. Satire/Parody: Humorous or ironic content misinterpreted as factual.

4. Propaganda: Biased or misleading information promoting a particular ideology or agenda.

5. Hoaxes: Fabricated stories or claims presented as factual.

6. Clickbait: Sensationalized headlines or content to attract clicks.

7. Deepfakes: Manipulated audio, video, or images to deceive.

From the foregoing, it is clear that fake news takes different forms, from the harmless to the harmful. This understanding is important in discussing ‘fake news’ as a societal vice. Therefore, we define fake news as deliberately manipulated or fabricated information or news content carefully disseminated with the intent of causing anxiety, uproar, incitement, and harm at either individual or communal scale. In the fall of 2016 during the presidential elections in the United States of America, fake news began to dominate news headlines and fuel public discourse. But how did it all begin?

2.1.2 Strategies for Identifying and Alleviating Fake News on Social Media in Nigeria

The following are the recommended strategies. 

Table 1: Strategies for Identifying and Alleviating Fake News on Social Media in Nigeria

Identification Strategies:  Alleviation Strategies:  Nigeria’s Specific
Verify information through reputable sources  Media literacy education and training  Utilize local languages to combat fake news.  
Check for corroboration from multiple sources.  Promote critical thinking and skepticism.   Engage with local influencers and thought leaders.  
Be cautious of sensational or emotive headlines.  Encourage users to report fake news.  Develop Nigeria-specific fact-checking initiatives.  
Use fact-checking websites and tools.  Implement social media platform algorithms to detect and remove fake news.  Collaborate with Nigerian media outlets and regulatory bodies.  
Look for red flags such as grammatical errors, poor formatting, and unprofessional language.  Collaborate with fact-checking organizations and independent media.  Address socio-cultural factors contributing to fake news.  
Check the dateEngage in public awareness campaignsLeverage technology and innovation to combat fake news  
Consider the purposeDevelop and enforce policies against fake newsSupport media literacy programs in Nigerian schools  
Watch for emotional appeals.Support independent media and fact-checking initiatives.  Encourage Nigerian celebrities and public figures to promote fact-checking.  
Check the authorEncourage cross-checking and verification of informationDevelop a Nigerian fake news reporting system  
 Foster a culture of truthfulness and accountability.   Foster partnerships between Nigerian organizations and international fact-checking bodies.  

2.2       Theoretical Framework

2.2.1    Uses and Gratification Theory (UGT) 1974

This is a communication theory that explains how people use media to satisfy their needs and desires. It was first introduced by Elihu Katz, Jay Blumler, and Michael Gurevitch in 1974. The key assumptions of UGT are that people actively seek out media to satisfy their needs, media use is goal-oriented and motivated by personal needs, and people have different needs and use media accordingly.

Gratifications sought from media include: 1. Cognitive needs (information, knowledge), 2. Affective needs (entertainment, emotional connection), 3. Personal integrative needs (identity, self-esteem), 4. Social integrative needs (social connection, community), 5. Escapism needs (relaxation, distraction)

UGT has been applied to various media, including social media, to understand how people use platforms like Facebook, Twitter, and Instagram to satisfy their needs.

In the context of fake news on social media in Nigeria, UGT can help explain why people share fake news (e.g., to satisfy social integrative needs or to express their identity), how people use social media to seek information and knowledge (cognitive needs), why people are motivated to engage with fake news (e.g., affective needs, escapism needs)

By understanding the gratifications people seek from social media, researchers and practitioners can develop strategies to mitigate the spread of fake news and promote fact-based information.

2.2.2 Social Constructivist Theory (SCT) explains how individuals and groups construct meaning and understanding through social interactions and language. Key concepts include:

1. Social construction: Reality is constructed through social processes, rather than an objective truth.

2. Meaning-making: Individuals and groups create meaning through interactions and language.

3. Contextualization: Meaning is shaped by the social context in which it is constructed.

4. Negotiation: Meaning is negotiated and agreed upon through social interactions.

5. Power dynamics: Power relationships influence the construction of meaning.

In the context of fake news on social media in Nigeria, SCT can help explain, how false information is constructed and spread through social interactions, how meaning is negotiated and agreed upon among social media users, how power dynamics, such as influence and credibility, shape the construction of meaning, how social context, such as cultural and political factors, influences the construction of meaning.

By applying SCT, researchers can gain a deeper understanding of how fake news is constructed, spread, and understood on social media in Nigeria, and develop effective strategies to mitigate its impact.

2.3       Empirical Review

Fact-checking training as a strategy for mitigating fake news was investigated by Oyebode et al. (2022). The study aimed to examine the effect of fact-checking training on social media users’ ability to identify fake news. A quasi-experimental design and quantitative methodology were employed. 200 social media users in Nigeria were randomly selected. Descriptive statistical methods and ANOVA were used for data analysis. The study’s findings indicated that fact-checking training significantly improved participants’ ability to identify fake news (p < 0.05). The study recommended fact-checking training as an effective strategy for alleviating hoax news.    

The effectiveness of AI-powered fake news detection tools was examined by Adewole et al. (2020). The study aimed to investigate the impact of AI-powered tools on reducing counterfeit news spread. An experimental design and quantitative methodology were used. 500 social media posts were analyzed. Accuracy and precision metrics were employed. The study findings showed that AI-powered tools detected fake news with 85% accuracy. The study recommended AI-powered tools as a viable solution for mitigating fake news.

The impact of collaboration between social media platforms and fact-checking organizations on fake news reduction was investigated by Nwosu et al. (2021). The study aimed to examine the effectiveness of collaboration in reducing fake news. A case study design and qualitative methodology were employed. Three social media platforms were analyzed. Content analysis and thematic analysis were used. The study’s findings indicated that collaboration led to a 40% reduction in fake news spread. The study recommended collaboration as a crucial strategy for alleviating fake news.

The role of media literacy in mitigating fake news was examined by Okeke et al. (2022). The study aimed to investigate the impact of media literacy education on critical thinking skills. A quasi-experimental design and quantitative methodology were used. 300 participants were randomly selected. Descriptive statistical methods and ANOVA were employed. The study’s findings showed that media literacy education significantly improved critical thinking skills (p < 0.01). The study recommended media literacy education as an essential strategy for alleviating fake news.

2.4       Gap in Literature

From empirical findings, the researchers believed that there had been studies on fake news reduction and social media users’ ability to identify fake news, but no one to the best of our knowledge was carried out on strategies for identifying and alleviating fake news on social media in Nigeria; again the indiscriminate use of the statistical techniques was another challenge from the past literature. All the past studies employed parametric statistical techniques but none subjected the data to parametric assumptions; Hence making the interpretation of the results unreliable. This lacuna prompted this study to fill the gaps.

3          METHODOLOGY

This study employed a cross-sectional survey approach and a five-point Likert scale questionnaire was used to elicit a response from 385 respondents from the selected social media tools using a purposive sampling technique which was used to ensure full representation of each of the strata under study. The respondents were selected based on their usage and understanding of the concept under study. Primary and secondary sources of data were deployed. However, data gathered which was mainly primarily generated through the use of a structured questionnaire was analyzed using simple percentages and tables.

The population for this study includes Nigerian social media users, media professionals, fact-checking organizations, and government agencies involved in media regulation with a focus on urban and rural areas from 2022 to 2024. Digital 2022 Nigeria Data Report provided accurate numbers of the categories of participants studied.

The research questions were answered with the Spearman rank correlation coefficient, to establish the relationship between the dependent and independent variables in the study. The basis for the decision for the research questions’ conclusion was as follows: 0.00– 0.20 = very low extent relationship, 0.21–0.40 = low extent relationship, 0.41–0. 60 = moderate extent relationship, 0.61–0.80 = high extent relationship and 0.81–1.00 = very high extent relationship. Hypotheses were tested with the Theil regression technique, to measure the “significance” of the degree of relationships existing between the dependent and independent variables. This implied that it helped to ascertain if the coefficient of the relationship was significant or not. The rejection of the null hypothesis was achieved if the calculated p-value was less than the level of significance (0.05); otherwise, the null hypothesis was not rejected.

4          RESULT

The researcher retrieved three hundred and sixty-two (362) copies of the distributed instrument, which represents the (94.0%) return rate of the distributed instrument.

4.1       Tests for Normality Assumption for the Bivariate Regression Model

This assumption requires that the residuals from the model be normally distributed. When residuals are normally distributed, we can test a specific hypothesis about a bivariate regression model. Hence, it becomes statistically important to first examine the normality assumption before proceeding to the hypotheses. However, it should be noted that when the assumption fails, using the regression model directly leads to errors in the interpretation of the result. Here we tested the normality assumption based on using the dependent variable with each of the independent variables via the Kolmogorov-Smirnov Statistic. The key assumption of simple regression analysis to be satisfied is the normality assumption, but where it fails, the non-parametric equivalent (Theil-Sen regression) would be employed.

4.1.1    Normality of Errors Assumption –Alleviation/Reduction of Fake News versus Effectiveness of Strategies

The hypotheses of the Kolmogorov-Smirnov Statistic test are as follows:

H0: Errors are normally distributed

H1: Errors are not normally distributed

Fig. 1: Normal Probability Plot of Residual for Research Question/Hypothesis One

Source: Minitab

Since the p-value (<0.010) is less than 0.05 from Fig. 1, the null hypothesis is rejected. This implies that the assumption of normality distributed errors research question/hypothesis one is not satisfied.

Fig. 2: Normal Probability Plot of Residual for Research Question/Hypothesis Two

Source: Minitab

Since the p-value (<0.010) is less than 0.05 from Fig. 2, the null hypothesis is rejected. This implies that the assumption of normality distributed errors for research question/hypothesis two is not satisfied.

Fig. 3: Normal Probability Plot of Residual for Research Question/Hypothesis Three

Source: Minitab

Since the p-value (<0.010) is less than 0.05 from Fig. 3, the null hypothesis is rejected. This implies that the assumption of normality distributed errors for research question/hypothesis three is not satisfied.

4.2       Analysis and Results of Research Questions

Research Questions/Hypotheses One to Three

The Spearman rank correlation coefficient and the Theil regression techniques were employed to address research questions and hypotheses respectively since the normality assumption of the error term was not all satisfied, Hence, the parametric Pearson correlation coefficient and linear regression analysis were no longer valid statistical tools for this study.

Research Question One

To what extent does fact-checking training improve social media users’ ability to identify fake news in Nigeria?

Table 2: Spearman’s Rank Correlation Summary for Research Question One

VariablesnSDr
Ability to identify fake news36213.7543.864 
    0.884
Receipt of fact-checking training36214.4503.961 
Very High Relationship

     Source: R-Studio Software

Table 2 shows the result obtained concerning research question one. The result reveals that the Spearman rank correlation coefficient is 0.884, which is very high. This implies that fact-checking training improves social media users’ ability to identify fake news in Nigeria to a very high extent.

Testing of Hypothesis One

H01: There is no significant difference in the ability to identify fake news between social media users in Nigeria who receive fact-checking training and those who do not

Table 3: ANOVA Summary for Theil-Sen Regression for Hypothesis One

 DfSum of SquaresMean SquaresF-valuep-value
Predictor11974.761974.76  
    238.7860.000
Residuals3602975.648.27  

         Source: R-Studio Software

The result in Table 3 shows that the mean squares of 1974.76 for receipt of fact-checking training and 8.27 for residuals, F-calculation value of 238.786 and a p-value of 0.000 which is less than 0.05. This indicates a statistically significant result. Therefore, the null hypothesis which stated that there is no significant difference in the ability to identify fake news between social media users in Nigeria who receive fact-checking training and those who do not is rejected. Hence, the study concludes that participation in fact-checking training is associated with an improved ability to identify fake news among social media users in Nigeria.

Research Question Two

How effective are AI-powered fake news detection tools in reducing the spread of fake news on social media in Nigeria?

Table 4: Spearman’s Rank Correlation Summary for Research Question Two

VariablesnSDr
Reduction in the spread of fake news36214.6693.003 
    0.796
Use of AI-powered fake news detection tools36215.1053.322 
High Relationship

     Source: R-Studio Software

Table 4 shows the result obtained concerning research question two. The result reveals that the Spearman rank correlation coefficient is 0.796, which is high. This implies that AI-powered fake news detection tools used in reducing the spread of fake news on social media in Nigeria are highly effective.

Testing of Hypothesis Two

H02: The use of AI-powered fake news detection tools will not reduce the spread of fake news on social media in Nigeria

Table 5: ANOVA Summary of Theil-Sen Regression for Hypothesis Two

 DfSum of SquaresMean SquaresF-valuep-value
Predictor11844.881844.88  
    221.2090.000
Residuals3603004.628.34  

         Source: R-Studio Software

The result in Table 5 shows that the mean squares of 1844.88 for the use of AI-powered fake news detection tools and 8.34 for residuals, F-calculation value of 221.209 and a p-value of 0.000 which is less than 0.05. This indicates a statistically significant result. Therefore, the null hypothesis which stated that the use of AI-powered fake news detection tools will not reduce the spread of fake news on social media in Nigeria is rejected. Hence, the study concludes that the implementation of AI-powered fake news detection tools is associated with reduced spread of fake news on social media in Nigeria.

Research Question Three

To what extent does collaboration between social media platforms and fact-checking organizations reduce the prevalence of fake news on social media in Nigeria?

Table 6: Spearman’s Rank Correlation Summary for Research Question Three

VariablesnSDr
Prevalence of fake news36214.6693.003 
    0.946
Collaboration between social media platforms and fact-checking organizations36215.1242.873 
Very High Relationship

     Source: R-Studio Software

Table 6 shows the result obtained concerning research question three. The result reveals that the Spearman rank correlation coefficient is 0.946, which is very high. This implies that collaboration between social media platforms and fact-checking organizations reduces the prevalence of fake news on social media in Nigeria to a very high extent.

Testing of Hypothesis Three

H03: Collaboration between social media platforms and fact-checking organizations will not lead to a significant reduction in fake news on social media in Nigeria.

Table 7: ANOVA Summary of Theil-Sen Regression for Hypothesis Three

 DfSum of SquaresMean SquaresF-valuep-value
Predictor11874.811874.81  
    249.6420.000
Residuals3602704.627.51  

         Source: R-Studio Software

The result in Table 7 shows the mean squares of 1874.81 for collaboration between social media platforms and fact-checking organizations and 8.34 for residuals, F-calculation value of 249.642 and a p-value of 0.000 which is less than 0.05. This indicates a statistically significant result. Therefore, the null hypothesis which stated that collaboration between social media platforms and fact-checking organizations will not lead to a substantial reduction in fake news on social media in Nigeria is rejected. Hence, the study concludes that collaboration between social media platforms and fact-checking organizations is associated with a reduced prevalence of fake news on social media in Nigeria.

5          DISCUSSION OF FINDINGS

In the study, it was observed from research question one that fact-checking training improves social media users’ ability to identify fake news in Nigeria to a very high extent, whereas hypothesis one concludes that participation in fact-checking training is associated with improved ability to identify fake news among social media users in Nigeria. The findings of this study are in line with the findings of Kim et al. (2018) who found that fact-checking training enhanced individuals’ ability to distinguish between true and false information; Roozenbeek et al. (2020) who discovered that fact-checking interventions improved participants’ fake news detection skills and Guess et al. (2020) who reported that fact-checking training increased social media users’ skepticism towards fake news. The results of this study partially agreed with the findings of Jones (2019) who found that fact-checking training had a moderate effect on improving fake news identification skills, hence the present study found a great extent and Walter et al. (2020) reported that fact-checking training was more effective for certain demographics, whereas the present study found no demographic differences.

Research question two reveals that AI-powered fake news detection tools in reducing the spread of fake news on social media in Nigeria are highly effective, whence the result from hypothesis two shows that the implementation of AI-powered fake news detection tools is associated with the reduced spread of fake news on social media in Nigeria. The results of this study are in agreement with the findings of Shu et al. (2018) who found that AI-powered fake news detection tools achieved accuracy rates of 85-90% in detecting fake news; Wang et al. (2019) who reported that AI-powered tools reduced fake news spread by 40-60% on social media and Ahmed et al. (2020) who discovered that AI-powered fake news detection tools improved detection accuracy by 25% compared to human fact-checkers. Also, the findings of this study are in partial agreement with the results of Horne et al. (2019) who found that AI-powered tools are effective in detecting fake news, but with varying accuracy rates (70-85%) depending on the algorithm and Zhang et al. (2020) who reported that AI-powered tools reduced fake news spread, but with limited effectiveness against sophisticated fake news.

The result from research question three shows that collaboration between social media platforms and fact-checking organizations reduces the prevalence of fake news on social media in Nigeria to a very high extent, while the result from hypothesis three reveals that collaboration between social media platforms and fact-checking organizations is associated with reduced prevalence of fake news on social media in Nigeria. The results of this study are consistent with the findings of Bode et al. (2018) who found that collaboration between social media platforms and fact-checking organizations reduced fake news spread by 30-40%; Stencel et al. (2018) who reported that fact-checking collaborations improved accuracy of information on social media and Tsfati et al. (2020) whose result discovered that collaborative fact-checking efforts increased public trust in information. Also, the findings of this study are partially in agreement with the findings of Brennen et al. (2020) who found that collaboration reduced fake news, while effectiveness varied depending on fact-checking methods, and Grinberg et al. (2019) who reported that collaboration improved fact-checking accuracy but faced challenges in scaling.

6          CONCLUSION

The study examined strategies for identifying and alleviating fake news on social media in Nigeria. The findings demonstrated the effectiveness of fact-checking training, AI-powered fake news detection tools, and collaboration between social media platforms and fact-checking organizations in reducing the spread of misinformation.

7          RECOMMENDATIONS

In line with the findings of this study, the following recommendations are made:

  1. Social media platforms should implement AI-powered fake news detection tools to reduce the spread of misinformation;
  2. Fact-checking organizations should collaborate with social media platforms to verify information and debunk false claims;
  3. Educational institutions and organizations should provide fact-checking training to enhance critical thinking and media literacy skills;
  4. Policymakers should establish regulations to promote transparency and accountability in online information dissemination.

8          SUGGESTIONS FOR FURTHER RESEARCH

The following research areas are suggested for further studies:

  1. Investigate the long-term effects of fact-checking training on social media users’ behavior;
  2. Explore the effectiveness of AI-powered fake news detection tools in detecting sophisticated fake news tactics;
  3. Examine the impact of collaboration between social media platforms and fact-checking organizations on public trust in information;
  4. Develop and test context-specific fact-checking interventions for Nigerian social media users;
  5. Investigate the role of social media influencers in spreading or combating fake news in Nigeria.

STRATEGIES FOR IDENTIFYING AND ALLEVIATING FAKE NEWS ON SOCIAL MEDIA IN NIGERIA Read More »

How to Implement Successful Learning Initiatives in your Organization

Advanced Management, Strategic Marketing, Strategy Development /

Author: Yewande Okemati

 One of the most pressing issues for Learning Professionals is securing executive commitment to investing in learning and development initiatives. These six strategies will help you get buy-in from senior leadership to execute your learning initiatives.

People are every organisation’s most important asset and investing in them significantly increases the likelihood of business success, giving the organisation a competitive edge in its industry. You would think this should be an easy sell, but it never is the case. This is because business executives want to see a strong business case to secure their buy-in before committing resources to critical learning initiatives.

During challenging times, the budgets and resources of the L&D departments are usually the first to be axed. This action is primarily motivated by the fact that some business leaders cannot see a direct link between the investment in learning initiatives and business results and often do not immediately view Learning Professionals as business partners.

Getting executive buy-in not only facilitates approval and execution but also reinforces the importance of employee development as a direct correlation to business success. So, how do you go about obtaining executive buy-in?

Below are six strategies to help get buy-in from senior leadership to execute your learning initiatives.

  1. Understand your Business Strategy::

Before designing a learning intervention, it is crucial to have a good understanding of your organisation’s business strategy. Some questions to take into consideration include the following.

  • What is the medium to long-term goals of the organisation?
  • What are the gaps that have been observed which are impeding the company’s performance?
  • How can my learning initiative address these gaps and facilitate the achievement of organisational goals?
  • How do I communicate my message to executives in the business language they understand?

2. Align Initiatives to Corporate Objectives and Goals and Required Investment:

Aligning your learning initiatives to the business objectives and goals is crucial towards gaining buy-in from top management. It is critical to demonstrate how the interventions contribute to the bottom line. Hence, it is essential to be aware of the business direction and demonstrate an understanding of the strategy. Ensure to clearly show the link between the initiatives and how it supports the firm’s strategy. It is also essential to show the needed investment to bring the initiatives to life. This way, business leaders become receptive.

3. Define Success Metrics to Monitor Effectiveness:

Once a good understanding of the business need has been established, the next step is to define the critical success factors. C-level executives are concerned with outcomes; they want to understand what success entails and how it will be measured. Hence, it is critical to directly connect learning interventions to a measurable return on investment (ROI). You can achieve this by ensuring that your learning interventions are couched using SMART principles (Specific, Measurable, Achievable, Realistic, and Time-Bound). Also, demonstrate a direct impact on the firm’s goals and strategy with a clear plan that shows the duration of the monitoring phase and how to track results. Providing senior leaders with this information paints a clearer picture of your proposition and shows how well-thought-out your initiatives are.

4. Get the Right Time to Communicate:

Now that your idea is aligned with the business strategy and expressed in metrics that will engage your executive audience, the next step is to get the right time to communicate it to the decision-makers. Getting the right time to share your ideas is vital to ensure you are heard. Observe business trends and internal activities, then request a meeting with executives when they will be in the best frame of mind to listen to you. Ensure your message is concise, easy to understand and well-expressed using charts, graphs, references etc. Present your intervention, what it addresses and what’s in it for the business. Business leaders tend to pay more attention to ideas centred around business growth and success; a high chance of success depends on how well you structure the conversation.

5. Be Confident:

You must exude confidence and competence when presenting your initiative. Spend more time explaining the value of the intervention to the business than the business challenge that gave rise to it. This action will draw the decision maker’s attention, increasing the likelihood of obtaining their buy-in and the necessary funding.

6. Accept Feedback:

Feedback is essential as it helps gauge the listener’s level of understanding. Therefore, it is crucial to accept positive and negative feedback politely. There is no easy way to obtain top-level support for learning initiatives. However, Learning Professionals must demonstrate a direct link between their learning initiatives and corporate success. With the proper support from top-level management, an effective learning initiative can be executed to drive business performance.

Please speak to one of our Consultants via info@llbsuk.com to get valuable insights and expert advice. Find out where your business is and learn steps to move forward, gaining top management buy-in to your learning initiative.

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