Table 3.
Literature synthesis of Social Media influence
| Reference | Problem | Concept | Network | Proved |
|---|---|---|---|---|
| Bao et al. (2013) | Predict users’ interests | Predict users’ interests: theoretical ideas and integration and time information | Micro-blogging | The accuracy of users’ interest predictions |
| Bian et al. (2014a) | Predicting Trending Messages and Diffusion Participants | Epidemic-oriented, interest-oriented, and social-oriented influence | Micro-blogging | The superiority of this method |
| Budak et al. (2014) | Inferring User Interests | Probabilistic model for people’s preferences throughout time | Twitter data | The most direct measurement and the top five interesting have a precision of 0,9 |
| Chader et al. (2017) | All friends are not equal | Closest relationships to the profiled user discover more useful information about him | Egocentric networks | Relevance of their prior hypothesis |
| Dang et al. (2016) | Emerging topics | Use dynamic changes to recognize new trends. | Micro-blogging networks | Effectiveness |
| Jia et al. (2017) | Inferring User Attributes | Integrates behaviors and social graphs, positive and negative | Google+ dataset |
- AttriInfer outperforms: efficiency and flexibility - AttriInfer’s optimized version is more flexible |
| Shah et al. (2018) | User Interest | Activities are a part of both the user’s and the network’s actions | General | Quite effective |
| Han et al. (2015) | Alike People, Alike Interests | Infer users’ interest similarity: demographic information, friendships, and interests | Facebook data | More similar likes with similar demographic information(age, location) |
| Wang et al. (2018) | Oriented interest extraction | UNITE for collecting interests using textual and structural information | Microblog | Outperforms the baseline methods significantly |
| Saez-Trumper et al. (2012) | Finding Trendsetters | A robust method for rating trendsetters of innovation diffusion |
- The ability to locate a large fraction of trend-setters. - Nodes having a high degree, the time corrosion function decreases |
|
| Xie et al. (2014) | Community-aware |
-User-generated tags and image correlation. - The multi-faceted folksonomy graph (MFG) |
NUS-wide dataset | Outperforms the preceding ones. |
| Li et al. (2012) | User Profiling | UDI determines how likely a user can follow others | Big volume of data | Effectiveness |
| Xiang et al. (2010) | Relationship Strength | Relationship strength assessment using a latent variable model | Facebook and LinkedIn | The graph auto-correlation and classification performance |
| Zhang et al. (2015) | Inferring User Attributes | Predict hashtags | General | Outperform |
| Zarrinkalam et al. (2017) | User interest | Inferred interests as a link prediction problem using a graph description model | General | Significantly improved |
| Zarrinkalam et al. (2019) | User interest prediction |
- User interests vary with time. - Prediction |
Better performance | |
| Wang et al. (2020) | The power of opinion |
- Propagation trends - Growth of KOL group networks and UGC keywords |
Sina Weibo |
- Information propagation - Slow growth is hitting |
| Harrigan et al. (2021) | Identifying influencers | Influential mavens | Decision-makers | |
| Jain and Sinha (2020) | Influence measure | Weighted Correlated Influence (WCI) |
-The most followers or the highest number of tweets -Trend-specific influence measurements are insufficient |
|
| Mabrouk et al. (2020) | Profile Classification |
- Hybridization of ontology and linear SVM - Hybridization of ontology and FSVM. |
General | Semantic fuzzy SVM classifiers perform well |
| Chia et al. (2021) | Ideal social media influencers |
- Combining the social capital and socialization theories with the social theories of learning - Framework identifying ideal SMIs |
General | The structural and relational dimensions influenced SMIs’ propensity |
| Yang et al. (2014) | Sybils in the Wild |
-Use ground-truth data about the behavior of Sybils in the wild to create a measurement-based, real-time Sybil detector - Characterization of Sybil graph topology on a major OSN |
OSNs |
-Still act with no explicit social ties -Effective |
| More and Lingam (2019) | Optimizing time for influence diffusion | A novel methodology based on gradient approach | SNAP and SLNDC |
The greedy algorithm only finds local minimum influence spread -Poorly and limited |
| Arora et al. (2019) | Measuring the Social Media Influencer Index | A mechanism for measuring the influencer index across popular social media platforms | Facebook, Twitter, and Instagram | In the highest accuracy of 93.7% followed by the KNN regression with 93.6% |
| Mahajan and Kaur (2021) | Social influence | A novel event recommendation system to suggest an event where the chances of a user’s participation are high | IoTCFR- IoT data | Better recommendation quality |
| Hodas et al. (2016) | The User’s Personality Influences Content Engagement | An experiment combining electroencephalograms, personality surveys, and prompts | EEG Data | Personality and mood are highly correlated between friends via homophily |
| Bohacik et al. (2017) | Detecting Compromised Accounts | An anomaly model trained on the previous login data of users | Pokec | A real potential |
| Chaabani and Akaichi (2022) | Terrorist communities’ evolution detection |
-An Artificial Bee Colony optimization -BCTTC to track terrorist evolution |
the Global Terrorism Database | Good results for small and large communities |
| Aswani et al. (2017) | Identifying buzz in social media |
-A hybrid artificial bee colony approach is integrated with k-nearest neighbors to identify and segregate buzz -A proposed hybrid bio-inspired approach |
Successfully giving an accuracy of 98.37% |