### Abstract

In this work, we study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or "good") campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. We show that this optimization problem is NP-hard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. We experimentally compare the performance of the greedy method to various heuristics. The experiments reveal that in most cases inexpensive heuristics such as degree centrality compare well with the greedy approach. We also study the influence limitation problem in the presence of missing data where the current states of nodes in the network are only known with a certain probability and show that prediction in this setting is a supermodular problem. We propose a prediction algorithm that is based on generating random spanning trees and evaluate the performance of this approach. The experiments reveal that using the prediction algorithm, we are able to tolerate about 90% missing data before the performance of the algorithm starts degrading and even with large amounts of missing data the performance degrades only to 75% of the performance that would be achieved with complete data.

Original language | English |
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Title of host publication | Proceedings of the 20th International Conference on World Wide Web, WWW 2011 |

Pages | 665-674 |

Number of pages | 10 |

DOIs | |

Publication status | Published - 1 Dec 2011 |

Externally published | Yes |

Event | 20th International Conference on World Wide Web, WWW 2011 - Hyderabad, India Duration: 28 Mar 2011 → 1 Apr 2011 |

### Other

Other | 20th International Conference on World Wide Web, WWW 2011 |
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Country | India |

City | Hyderabad |

Period | 28/3/11 → 1/4/11 |

### Fingerprint

### Keywords

- Competing campaigns
- Information cascades
- Misinformation
- Social networks
- Submodular functions
- Supermodular functions

### ASJC Scopus subject areas

- Computer Networks and Communications

### Cite this

*Proceedings of the 20th International Conference on World Wide Web, WWW 2011*(pp. 665-674) https://doi.org/10.1145/1963405.1963499

**Limiting the spread of misinformation in social networks.** / Budak, Ceren; Agrawal, Divyakant; Abbadi, Amr El.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 20th International Conference on World Wide Web, WWW 2011.*pp. 665-674, 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, 28/3/11. https://doi.org/10.1145/1963405.1963499

}

TY - GEN

T1 - Limiting the spread of misinformation in social networks

AU - Budak, Ceren

AU - Agrawal, Divyakant

AU - Abbadi, Amr El

PY - 2011/12/1

Y1 - 2011/12/1

N2 - In this work, we study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or "good") campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. We show that this optimization problem is NP-hard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. We experimentally compare the performance of the greedy method to various heuristics. The experiments reveal that in most cases inexpensive heuristics such as degree centrality compare well with the greedy approach. We also study the influence limitation problem in the presence of missing data where the current states of nodes in the network are only known with a certain probability and show that prediction in this setting is a supermodular problem. We propose a prediction algorithm that is based on generating random spanning trees and evaluate the performance of this approach. The experiments reveal that using the prediction algorithm, we are able to tolerate about 90% missing data before the performance of the algorithm starts degrading and even with large amounts of missing data the performance degrades only to 75% of the performance that would be achieved with complete data.

AB - In this work, we study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or "good") campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. We show that this optimization problem is NP-hard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. We experimentally compare the performance of the greedy method to various heuristics. The experiments reveal that in most cases inexpensive heuristics such as degree centrality compare well with the greedy approach. We also study the influence limitation problem in the presence of missing data where the current states of nodes in the network are only known with a certain probability and show that prediction in this setting is a supermodular problem. We propose a prediction algorithm that is based on generating random spanning trees and evaluate the performance of this approach. The experiments reveal that using the prediction algorithm, we are able to tolerate about 90% missing data before the performance of the algorithm starts degrading and even with large amounts of missing data the performance degrades only to 75% of the performance that would be achieved with complete data.

KW - Competing campaigns

KW - Information cascades

KW - Misinformation

KW - Social networks

KW - Submodular functions

KW - Supermodular functions

UR - http://www.scopus.com/inward/record.url?scp=80052722917&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80052722917&partnerID=8YFLogxK

U2 - 10.1145/1963405.1963499

DO - 10.1145/1963405.1963499

M3 - Conference contribution

SN - 9781450306324

SP - 665

EP - 674

BT - Proceedings of the 20th International Conference on World Wide Web, WWW 2011

ER -